Add bert encoder test with fake weights. (#3345)
This is generated by
1) Hack on mandelbrot_test.py to dump IRs to a file
* Load the model from keras
* Apply `tf_signature_def_saved_model_to_compiler_module` on the model
* Call `to_asm()` to get MLIR text
2) Replace weights with fake weights
3) Drop attributes for module, func and flow.variable
diff --git a/iree/test/e2e/models/BUILD b/iree/test/e2e/models/BUILD
index e7ece7a..721f360 100644
--- a/iree/test/e2e/models/BUILD
+++ b/iree/test/e2e/models/BUILD
@@ -15,6 +15,7 @@
# Tests for end-to-end IREE support of entire models or their close derivatives.
load("//iree:lit_test.bzl", "iree_lit_test_suite")
+load("//build_tools/bazel:iree_check_test.bzl", "iree_check_single_backend_test_suite")
package(
default_visibility = ["//visibility:public"],
@@ -22,12 +23,24 @@
licenses = ["notice"], # Apache 2.0
)
+CHECK_FRAMEWORK_TESTS = ["bert_encoder_unrolled_fake_weights.mlir"]
+
iree_lit_test_suite(
name = "lit",
- srcs = glob(["*.mlir"]),
+ srcs = glob(
+ ["*.mlir"],
+ exclude = CHECK_FRAMEWORK_TESTS,
+ ),
data = [
"//iree/tools:IreeFileCheck",
"//iree/tools:iree-run-mlir",
],
tags = ["hostonly"],
)
+
+iree_check_single_backend_test_suite(
+ name = "check_vmla_vmla",
+ srcs = CHECK_FRAMEWORK_TESTS,
+ driver = "vmla",
+ target_backend = "vmla",
+)
diff --git a/iree/test/e2e/models/CMakeLists.txt b/iree/test/e2e/models/CMakeLists.txt
index e2b4157..47b3f6d 100644
--- a/iree/test/e2e/models/CMakeLists.txt
+++ b/iree/test/e2e/models/CMakeLists.txt
@@ -15,6 +15,8 @@
iree_add_all_subdirs()
file(GLOB _GLOB_X_MLIR LIST_DIRECTORIES false RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} CONFIGURE_DEPENDS *.mlir)
+file(GLOB _GLOB_BERT_ENCODER_UNROLLED_FAKE_WEIGHTS_MLIR LIST_DIRECTORIES false RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} CONFIGURE_DEPENDS bert_encoder_unrolled_fake_weights.mlir)
+list(REMOVE_ITEM _GLOB_X_MLIR ${_GLOB_BERT_ENCODER_UNROLLED_FAKE_WEIGHTS_MLIR})
iree_lit_test_suite(
NAME
lit
@@ -26,3 +28,14 @@
LABELS
"hostonly"
)
+
+iree_check_single_backend_test_suite(
+ NAME
+ check_vmla_vmla
+ SRCS
+ "bert_encoder_unrolled_fake_weights.mlir"
+ TARGET_BACKEND
+ vmla
+ DRIVER
+ vmla
+)
diff --git a/iree/test/e2e/models/bert_encoder_unrolled_fake_weights.mlir b/iree/test/e2e/models/bert_encoder_unrolled_fake_weights.mlir
new file mode 100644
index 0000000..55a55ab
--- /dev/null
+++ b/iree/test/e2e/models/bert_encoder_unrolled_fake_weights.mlir
@@ -0,0 +1,6710 @@
+// MobileBert encoder model with placeholder weights, for testing.
+
+module {
+ flow.variable @"__iree_flow_bert/embeddings/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/embedding_transformation/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/embedding_transformation/kernel" dense<0.0001> : tensor<384x512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/position_embeddings" dense<0.0> : tensor<512x512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/token_type_embeddings" dense<0.0> : tensor<2x512xf32>
+ flow.variable @"__iree_flow_bert/embeddings/word_embeddings" dense<0.0> : tensor<30522x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_10/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_11/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_12/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_13/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_14/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_15/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_16/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_17/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_18/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_19/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_20/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_21/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_22/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_23/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_3/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_4/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_5/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_6/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_7/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_8/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/output/dense/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/key/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/key/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/query/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/query/kernel" dense<0.0001> : tensor<128x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/value/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/attention/self/value/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/input/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/input/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/input/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/bottleneck/input/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/intermediate/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/intermediate/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/FakeLayerNorm/beta" dense<1.0> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/FakeLayerNorm/gamma" dense<0.4> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/bottleneck/FakeLayerNorm/beta" dense<1.0> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/bottleneck/FakeLayerNorm/gamma" dense<0.4> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/bottleneck/dense/bias" dense<0.1> : tensor<512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/bottleneck/dense/kernel" dense<0.0001> : tensor<128x512xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/dense/bias" dense<0.1> : tensor<128xf32>
+ flow.variable @"__iree_flow_bert/encoder/layer_9/output/dense/kernel" dense<0.0001> : tensor<512x128xf32>
+ flow.variable @"__iree_flow_cls/squad/output_bias" dense<0.1> : tensor<2xf32>
+ flow.variable @"__iree_flow_cls/squad/output_weights" dense<1.0> : tensor<2x512xf32>
+ func @serving_default() {
+ %arg0 = iree.unfoldable_constant dense<0> : tensor<1x384xi32>
+ %arg1 = iree.unfoldable_constant dense<0> : tensor<1x384xi32>
+ %arg2 = iree.unfoldable_constant dense<0> : tensor<1x384xi32>
+ %0 = flow.variable.address @"__iree_flow_bert/embeddings/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %1 = flow.variable.address @"__iree_flow_bert/embeddings/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %2 = flow.variable.address @"__iree_flow_bert/embeddings/embedding_transformation/bias" : !iree.ptr<tensor<512xf32>>
+ %3 = flow.variable.address @"__iree_flow_bert/embeddings/embedding_transformation/kernel" : !iree.ptr<tensor<384x512xf32>>
+ %4 = flow.variable.address @"__iree_flow_bert/embeddings/position_embeddings" : !iree.ptr<tensor<512x512xf32>>
+ %5 = flow.variable.address @"__iree_flow_bert/embeddings/token_type_embeddings" : !iree.ptr<tensor<2x512xf32>>
+ %6 = flow.variable.address @"__iree_flow_bert/embeddings/word_embeddings" : !iree.ptr<tensor<30522x128xf32>>
+ %7 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %8 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %9 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %10 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %11 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %12 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %13 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %14 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %15 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %16 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %17 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %18 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %19 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %20 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %21 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %22 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %23 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %24 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %25 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %26 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %27 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %28 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %29 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %30 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %31 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %32 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %33 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %34 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %35 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %36 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %37 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %38 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %39 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %40 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %41 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %42 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %43 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %44 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %45 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %46 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %47 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %48 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %49 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %50 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %51 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %52 = flow.variable.address @"__iree_flow_bert/encoder/layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %53 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %54 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %55 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %56 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %57 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %58 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %59 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %60 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %61 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %62 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %63 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %64 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %65 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %66 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %67 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %68 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %69 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %70 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %71 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %72 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %73 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %74 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %75 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %76 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %77 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %78 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %79 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %80 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %81 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %82 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %83 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %84 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %85 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %86 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %87 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %88 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %89 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %90 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %91 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %92 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %93 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %94 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %95 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %96 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %97 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %98 = flow.variable.address @"__iree_flow_bert/encoder/layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %99 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %100 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %101 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %102 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %103 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %104 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %105 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %106 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %107 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %108 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %109 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %110 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %111 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %112 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %113 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %114 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %115 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %116 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %117 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %118 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %119 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %120 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %121 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %122 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %123 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %124 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %125 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %126 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %127 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %128 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %129 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %130 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %131 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %132 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %133 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %134 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %135 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %136 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %137 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %138 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %139 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %140 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %141 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %142 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %143 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %144 = flow.variable.address @"__iree_flow_bert/encoder/layer_10/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %145 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %146 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %147 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %148 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %149 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %150 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %151 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %152 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %153 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %154 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %155 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %156 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %157 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %158 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %159 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %160 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %161 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %162 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %163 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %164 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %165 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %166 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %167 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %168 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %169 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %170 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %171 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %172 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %173 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %174 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %175 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %176 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %177 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %178 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %179 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %180 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %181 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %182 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %183 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %184 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %185 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %186 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %187 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %188 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %189 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %190 = flow.variable.address @"__iree_flow_bert/encoder/layer_11/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %191 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %192 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %193 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %194 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %195 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %196 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %197 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %198 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %199 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %200 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %201 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %202 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %203 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %204 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %205 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %206 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %207 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %208 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %209 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %210 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %211 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %212 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %213 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %214 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %215 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %216 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %217 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %218 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %219 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %220 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %221 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %222 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %223 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %224 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %225 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %226 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %227 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %228 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %229 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %230 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %231 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %232 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %233 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %234 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %235 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %236 = flow.variable.address @"__iree_flow_bert/encoder/layer_12/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %237 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %238 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %239 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %240 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %241 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %242 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %243 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %244 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %245 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %246 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %247 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %248 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %249 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %250 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %251 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %252 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %253 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %254 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %255 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %256 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %257 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %258 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %259 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %260 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %261 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %262 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %263 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %264 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %265 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %266 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %267 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %268 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %269 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %270 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %271 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %272 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %273 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %274 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %275 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %276 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %277 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %278 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %279 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %280 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %281 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %282 = flow.variable.address @"__iree_flow_bert/encoder/layer_13/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %283 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %284 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %285 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %286 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %287 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %288 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %289 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %290 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %291 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %292 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %293 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %294 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %295 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %296 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %297 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %298 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %299 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %300 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %301 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %302 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %303 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %304 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %305 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %306 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %307 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %308 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %309 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %310 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %311 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %312 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %313 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %314 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %315 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %316 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %317 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %318 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %319 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %320 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %321 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %322 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %323 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %324 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %325 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %326 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %327 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %328 = flow.variable.address @"__iree_flow_bert/encoder/layer_14/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %329 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %330 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %331 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %332 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %333 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %334 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %335 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %336 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %337 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %338 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %339 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %340 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %341 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %342 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %343 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %344 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %345 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %346 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %347 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %348 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %349 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %350 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %351 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %352 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %353 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %354 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %355 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %356 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %357 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %358 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %359 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %360 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %361 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %362 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %363 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %364 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %365 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %366 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %367 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %368 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %369 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %370 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %371 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %372 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %373 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %374 = flow.variable.address @"__iree_flow_bert/encoder/layer_15/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %375 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %376 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %377 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %378 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %379 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %380 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %381 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %382 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %383 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %384 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %385 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %386 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %387 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %388 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %389 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %390 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %391 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %392 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %393 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %394 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %395 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %396 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %397 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %398 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %399 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %400 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %401 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %402 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %403 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %404 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %405 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %406 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %407 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %408 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %409 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %410 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %411 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %412 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %413 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %414 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %415 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %416 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %417 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %418 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %419 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %420 = flow.variable.address @"__iree_flow_bert/encoder/layer_16/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %421 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %422 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %423 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %424 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %425 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %426 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %427 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %428 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %429 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %430 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %431 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %432 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %433 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %434 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %435 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %436 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %437 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %438 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %439 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %440 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %441 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %442 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %443 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %444 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %445 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %446 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %447 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %448 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %449 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %450 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %451 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %452 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %453 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %454 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %455 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %456 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %457 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %458 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %459 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %460 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %461 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %462 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %463 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %464 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %465 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %466 = flow.variable.address @"__iree_flow_bert/encoder/layer_17/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %467 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %468 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %469 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %470 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %471 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %472 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %473 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %474 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %475 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %476 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %477 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %478 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %479 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %480 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %481 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %482 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %483 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %484 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %485 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %486 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %487 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %488 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %489 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %490 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %491 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %492 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %493 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %494 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %495 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %496 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %497 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %498 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %499 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %500 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %501 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %502 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %503 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %504 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %505 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %506 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %507 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %508 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %509 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %510 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %511 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %512 = flow.variable.address @"__iree_flow_bert/encoder/layer_18/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %513 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %514 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %515 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %516 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %517 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %518 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %519 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %520 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %521 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %522 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %523 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %524 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %525 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %526 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %527 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %528 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %529 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %530 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %531 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %532 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %533 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %534 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %535 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %536 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %537 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %538 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %539 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %540 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %541 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %542 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %543 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %544 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %545 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %546 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %547 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %548 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %549 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %550 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %551 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %552 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %553 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %554 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %555 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %556 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %557 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %558 = flow.variable.address @"__iree_flow_bert/encoder/layer_19/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %559 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %560 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %561 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %562 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %563 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %564 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %565 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %566 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %567 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %568 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %569 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %570 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %571 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %572 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %573 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %574 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %575 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %576 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %577 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %578 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %579 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %580 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %581 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %582 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %583 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %584 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %585 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %586 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %587 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %588 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %589 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %590 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %591 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %592 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %593 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %594 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %595 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %596 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %597 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %598 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %599 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %600 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %601 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %602 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %603 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %604 = flow.variable.address @"__iree_flow_bert/encoder/layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %605 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %606 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %607 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %608 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %609 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %610 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %611 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %612 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %613 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %614 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %615 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %616 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %617 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %618 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %619 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %620 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %621 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %622 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %623 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %624 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %625 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %626 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %627 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %628 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %629 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %630 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %631 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %632 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %633 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %634 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %635 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %636 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %637 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %638 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %639 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %640 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %641 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %642 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %643 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %644 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %645 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %646 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %647 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %648 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %649 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %650 = flow.variable.address @"__iree_flow_bert/encoder/layer_20/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %651 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %652 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %653 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %654 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %655 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %656 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %657 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %658 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %659 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %660 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %661 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %662 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %663 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %664 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %665 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %666 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %667 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %668 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %669 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %670 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %671 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %672 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %673 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %674 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %675 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %676 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %677 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %678 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %679 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %680 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %681 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %682 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %683 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %684 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %685 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %686 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %687 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %688 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %689 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %690 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %691 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %692 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %693 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %694 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %695 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %696 = flow.variable.address @"__iree_flow_bert/encoder/layer_21/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %697 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %698 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %699 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %700 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %701 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %702 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %703 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %704 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %705 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %706 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %707 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %708 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %709 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %710 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %711 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %712 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %713 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %714 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %715 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %716 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %717 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %718 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %719 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %720 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %721 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %722 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %723 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %724 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %725 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %726 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %727 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %728 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %729 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %730 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %731 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %732 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %733 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %734 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %735 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %736 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %737 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %738 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %739 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %740 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %741 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %742 = flow.variable.address @"__iree_flow_bert/encoder/layer_22/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %743 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %744 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %745 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %746 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %747 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %748 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %749 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %750 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %751 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %752 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %753 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %754 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %755 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %756 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %757 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %758 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %759 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %760 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %761 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %762 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %763 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %764 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %765 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %766 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %767 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %768 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %769 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %770 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %771 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %772 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %773 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %774 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %775 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %776 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %777 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %778 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %779 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %780 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %781 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %782 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %783 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %784 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %785 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %786 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %787 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %788 = flow.variable.address @"__iree_flow_bert/encoder/layer_23/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %789 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %790 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %791 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %792 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %793 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %794 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %795 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %796 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %797 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %798 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %799 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %800 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %801 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %802 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %803 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %804 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %805 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %806 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %807 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %808 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %809 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %810 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %811 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %812 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %813 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %814 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %815 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %816 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %817 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %818 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %819 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %820 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %821 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %822 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %823 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %824 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %825 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %826 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %827 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %828 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %829 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %830 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %831 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %832 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %833 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %834 = flow.variable.address @"__iree_flow_bert/encoder/layer_3/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %835 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %836 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %837 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %838 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %839 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %840 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %841 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %842 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %843 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %844 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %845 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %846 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %847 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %848 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %849 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %850 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %851 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %852 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %853 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %854 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %855 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %856 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %857 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %858 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %859 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %860 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %861 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %862 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %863 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %864 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %865 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %866 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %867 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %868 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %869 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %870 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %871 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %872 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %873 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %874 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %875 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %876 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %877 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %878 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %879 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %880 = flow.variable.address @"__iree_flow_bert/encoder/layer_4/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %881 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %882 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %883 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %884 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %885 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %886 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %887 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %888 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %889 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %890 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %891 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %892 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %893 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %894 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %895 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %896 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %897 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %898 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %899 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %900 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %901 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %902 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %903 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %904 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %905 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %906 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %907 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %908 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %909 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %910 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %911 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %912 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %913 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %914 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %915 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %916 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %917 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %918 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %919 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %920 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %921 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %922 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %923 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %924 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %925 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %926 = flow.variable.address @"__iree_flow_bert/encoder/layer_5/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %927 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %928 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %929 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %930 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %931 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %932 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %933 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %934 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %935 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %936 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %937 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %938 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %939 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %940 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %941 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %942 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %943 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %944 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %945 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %946 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %947 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %948 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %949 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %950 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %951 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %952 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %953 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %954 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %955 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %956 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %957 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %958 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %959 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %960 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %961 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %962 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %963 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %964 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %965 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %966 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %967 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %968 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %969 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %970 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %971 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %972 = flow.variable.address @"__iree_flow_bert/encoder/layer_6/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %973 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %974 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %975 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %976 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %977 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %978 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %979 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %980 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %981 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %982 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %983 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %984 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %985 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %986 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %987 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %988 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %989 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %990 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %991 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %992 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %993 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %994 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %995 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %996 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %997 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %998 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %999 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1000 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1001 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1002 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1003 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1004 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1005 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1006 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1007 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1008 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1009 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1010 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1011 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1012 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1013 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %1014 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %1015 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1016 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1017 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1018 = flow.variable.address @"__iree_flow_bert/encoder/layer_7/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1019 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1020 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1021 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1022 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1023 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %1024 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1025 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %1026 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1027 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %1028 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1029 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1030 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1031 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1032 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1033 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1034 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1035 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1036 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1037 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1038 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1039 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1040 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1041 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1042 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1043 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1044 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1045 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1046 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1047 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1048 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1049 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1050 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1051 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1052 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1053 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1054 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1055 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1056 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1057 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1058 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1059 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %1060 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %1061 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1062 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1063 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1064 = flow.variable.address @"__iree_flow_bert/encoder/layer_8/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1065 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1066 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1067 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1068 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/output/dense/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1069 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/key/bias" : !iree.ptr<tensor<128xf32>>
+ %1070 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/key/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1071 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/query/bias" : !iree.ptr<tensor<128xf32>>
+ %1072 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/query/kernel" : !iree.ptr<tensor<128x128xf32>>
+ %1073 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/value/bias" : !iree.ptr<tensor<128xf32>>
+ %1074 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/attention/self/value/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1075 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1076 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1077 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1078 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/attention/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1079 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/input/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1080 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/input/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1081 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/input/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1082 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/bottleneck/input/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1083 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1084 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1085 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1086 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1087 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1088 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_0/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1089 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1090 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1091 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1092 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1093 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1094 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_1/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1095 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1096 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1097 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1098 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1099 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1100 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/ffn_layer_2/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1101 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/intermediate/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1102 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/intermediate/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1103 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/FakeLayerNorm/beta" : !iree.ptr<tensor<128xf32>>
+ %1104 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/FakeLayerNorm/gamma" : !iree.ptr<tensor<128xf32>>
+ %1105 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/bottleneck/FakeLayerNorm/beta" : !iree.ptr<tensor<512xf32>>
+ %1106 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/bottleneck/FakeLayerNorm/gamma" : !iree.ptr<tensor<512xf32>>
+ %1107 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/bottleneck/dense/bias" : !iree.ptr<tensor<512xf32>>
+ %1108 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/bottleneck/dense/kernel" : !iree.ptr<tensor<128x512xf32>>
+ %1109 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/dense/bias" : !iree.ptr<tensor<128xf32>>
+ %1110 = flow.variable.address @"__iree_flow_bert/encoder/layer_9/output/dense/kernel" : !iree.ptr<tensor<512x128xf32>>
+ %1111 = flow.variable.address @"__iree_flow_cls/squad/output_bias" : !iree.ptr<tensor<2xf32>>
+ %1112 = flow.variable.address @"__iree_flow_cls/squad/output_weights" : !iree.ptr<tensor<2x512xf32>>
+ %1113 = mhlo.constant dense<-1.000000e+04> : tensor<1x1x384x384xf32>
+ %1114 = mhlo.constant dense<0.176776692> : tensor<1x4x384x384xf32>
+ %1115 = mhlo.constant dense<1.000000e+04> : tensor<1x1x384x384xf32>
+ %1116 = mhlo.constant dense<1.000000e+00> : tensor<1x384x384xf32>
+ %1117 = mhlo.constant dense<0xFF800000> : tensor<f32>
+ %1118 = mhlo.constant dense<0.000000e+00> : tensor<f32>
+ %1119 = mhlo.constant dense<0.000000e+00> : tensor<1x384x512xf32>
+ %1120 = flow.variable.load.indirect %0 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1121 = flow.variable.load.indirect %1 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1122 = flow.variable.load.indirect %2 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1123 = flow.variable.load.indirect %3 : !iree.ptr<tensor<384x512xf32>> -> tensor<384x512xf32>
+ %1124 = flow.variable.load.indirect %4 : !iree.ptr<tensor<512x512xf32>> -> tensor<512x512xf32>
+ %1125 = "mhlo.slice"(%1124) {limit_indices = dense<[384, 512]> : tensor<2xi64>, start_indices = dense<0> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} : (tensor<512x512xf32>) -> tensor<384x512xf32>
+ %1126 = "mhlo.reshape"(%1125) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %1127 = flow.variable.load.indirect %5 : !iree.ptr<tensor<2x512xf32>> -> tensor<2x512xf32>
+ %1128 = flow.variable.load.indirect %6 : !iree.ptr<tensor<30522x128xf32>> -> tensor<30522x128xf32>
+ %1129 = flow.variable.load.indirect %7 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1130 = flow.variable.load.indirect %8 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1131 = flow.variable.load.indirect %9 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1132 = flow.variable.load.indirect %10 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1133 = flow.variable.load.indirect %11 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1134 = flow.variable.load.indirect %12 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1135 = flow.variable.load.indirect %13 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1136 = flow.variable.load.indirect %14 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1137 = flow.variable.load.indirect %15 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1138 = flow.variable.load.indirect %16 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1139 = flow.variable.load.indirect %17 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1140 = flow.variable.load.indirect %18 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1141 = flow.variable.load.indirect %19 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1142 = flow.variable.load.indirect %20 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1143 = flow.variable.load.indirect %21 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1144 = flow.variable.load.indirect %22 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1145 = flow.variable.load.indirect %23 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1146 = flow.variable.load.indirect %24 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1147 = flow.variable.load.indirect %25 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1148 = flow.variable.load.indirect %26 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1149 = flow.variable.load.indirect %27 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1150 = flow.variable.load.indirect %28 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1151 = flow.variable.load.indirect %29 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1152 = flow.variable.load.indirect %30 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1153 = flow.variable.load.indirect %31 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1154 = flow.variable.load.indirect %32 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1155 = flow.variable.load.indirect %33 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1156 = flow.variable.load.indirect %34 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1157 = flow.variable.load.indirect %35 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1158 = flow.variable.load.indirect %36 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1159 = flow.variable.load.indirect %37 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1160 = flow.variable.load.indirect %38 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1161 = flow.variable.load.indirect %39 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1162 = flow.variable.load.indirect %40 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1163 = flow.variable.load.indirect %41 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1164 = flow.variable.load.indirect %42 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1165 = flow.variable.load.indirect %43 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1166 = flow.variable.load.indirect %44 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1167 = flow.variable.load.indirect %45 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1168 = flow.variable.load.indirect %46 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1169 = flow.variable.load.indirect %47 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1170 = flow.variable.load.indirect %48 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1171 = flow.variable.load.indirect %49 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1172 = flow.variable.load.indirect %50 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1173 = flow.variable.load.indirect %51 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1174 = flow.variable.load.indirect %52 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1175 = flow.variable.load.indirect %53 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1176 = flow.variable.load.indirect %54 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1177 = flow.variable.load.indirect %55 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1178 = flow.variable.load.indirect %56 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1179 = flow.variable.load.indirect %57 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1180 = flow.variable.load.indirect %58 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1181 = flow.variable.load.indirect %59 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1182 = flow.variable.load.indirect %60 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1183 = flow.variable.load.indirect %61 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1184 = flow.variable.load.indirect %62 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1185 = flow.variable.load.indirect %63 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1186 = flow.variable.load.indirect %64 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1187 = flow.variable.load.indirect %65 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1188 = flow.variable.load.indirect %66 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1189 = flow.variable.load.indirect %67 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1190 = flow.variable.load.indirect %68 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1191 = flow.variable.load.indirect %69 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1192 = flow.variable.load.indirect %70 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1193 = flow.variable.load.indirect %71 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1194 = flow.variable.load.indirect %72 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1195 = flow.variable.load.indirect %73 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1196 = flow.variable.load.indirect %74 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1197 = flow.variable.load.indirect %75 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1198 = flow.variable.load.indirect %76 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1199 = flow.variable.load.indirect %77 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1200 = flow.variable.load.indirect %78 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1201 = flow.variable.load.indirect %79 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1202 = flow.variable.load.indirect %80 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1203 = flow.variable.load.indirect %81 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1204 = flow.variable.load.indirect %82 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1205 = flow.variable.load.indirect %83 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1206 = flow.variable.load.indirect %84 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1207 = flow.variable.load.indirect %85 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1208 = flow.variable.load.indirect %86 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1209 = flow.variable.load.indirect %87 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1210 = flow.variable.load.indirect %88 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1211 = flow.variable.load.indirect %89 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1212 = flow.variable.load.indirect %90 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1213 = flow.variable.load.indirect %91 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1214 = flow.variable.load.indirect %92 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1215 = flow.variable.load.indirect %93 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1216 = flow.variable.load.indirect %94 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1217 = flow.variable.load.indirect %95 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1218 = flow.variable.load.indirect %96 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1219 = flow.variable.load.indirect %97 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1220 = flow.variable.load.indirect %98 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1221 = flow.variable.load.indirect %99 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1222 = flow.variable.load.indirect %100 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1223 = flow.variable.load.indirect %101 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1224 = flow.variable.load.indirect %102 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1225 = flow.variable.load.indirect %103 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1226 = flow.variable.load.indirect %104 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1227 = flow.variable.load.indirect %105 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1228 = flow.variable.load.indirect %106 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1229 = flow.variable.load.indirect %107 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1230 = flow.variable.load.indirect %108 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1231 = flow.variable.load.indirect %109 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1232 = flow.variable.load.indirect %110 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1233 = flow.variable.load.indirect %111 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1234 = flow.variable.load.indirect %112 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1235 = flow.variable.load.indirect %113 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1236 = flow.variable.load.indirect %114 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1237 = flow.variable.load.indirect %115 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1238 = flow.variable.load.indirect %116 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1239 = flow.variable.load.indirect %117 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1240 = flow.variable.load.indirect %118 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1241 = flow.variable.load.indirect %119 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1242 = flow.variable.load.indirect %120 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1243 = flow.variable.load.indirect %121 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1244 = flow.variable.load.indirect %122 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1245 = flow.variable.load.indirect %123 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1246 = flow.variable.load.indirect %124 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1247 = flow.variable.load.indirect %125 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1248 = flow.variable.load.indirect %126 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1249 = flow.variable.load.indirect %127 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1250 = flow.variable.load.indirect %128 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1251 = flow.variable.load.indirect %129 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1252 = flow.variable.load.indirect %130 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1253 = flow.variable.load.indirect %131 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1254 = flow.variable.load.indirect %132 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1255 = flow.variable.load.indirect %133 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1256 = flow.variable.load.indirect %134 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1257 = flow.variable.load.indirect %135 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1258 = flow.variable.load.indirect %136 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1259 = flow.variable.load.indirect %137 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1260 = flow.variable.load.indirect %138 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1261 = flow.variable.load.indirect %139 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1262 = flow.variable.load.indirect %140 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1263 = flow.variable.load.indirect %141 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1264 = flow.variable.load.indirect %142 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1265 = flow.variable.load.indirect %143 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1266 = flow.variable.load.indirect %144 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1267 = flow.variable.load.indirect %145 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1268 = flow.variable.load.indirect %146 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1269 = flow.variable.load.indirect %147 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1270 = flow.variable.load.indirect %148 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1271 = flow.variable.load.indirect %149 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1272 = flow.variable.load.indirect %150 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1273 = flow.variable.load.indirect %151 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1274 = flow.variable.load.indirect %152 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1275 = flow.variable.load.indirect %153 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1276 = flow.variable.load.indirect %154 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1277 = flow.variable.load.indirect %155 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1278 = flow.variable.load.indirect %156 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1279 = flow.variable.load.indirect %157 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1280 = flow.variable.load.indirect %158 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1281 = flow.variable.load.indirect %159 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1282 = flow.variable.load.indirect %160 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1283 = flow.variable.load.indirect %161 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1284 = flow.variable.load.indirect %162 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1285 = flow.variable.load.indirect %163 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1286 = flow.variable.load.indirect %164 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1287 = flow.variable.load.indirect %165 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1288 = flow.variable.load.indirect %166 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1289 = flow.variable.load.indirect %167 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1290 = flow.variable.load.indirect %168 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1291 = flow.variable.load.indirect %169 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1292 = flow.variable.load.indirect %170 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1293 = flow.variable.load.indirect %171 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1294 = flow.variable.load.indirect %172 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1295 = flow.variable.load.indirect %173 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1296 = flow.variable.load.indirect %174 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1297 = flow.variable.load.indirect %175 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1298 = flow.variable.load.indirect %176 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1299 = flow.variable.load.indirect %177 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1300 = flow.variable.load.indirect %178 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1301 = flow.variable.load.indirect %179 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1302 = flow.variable.load.indirect %180 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1303 = flow.variable.load.indirect %181 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1304 = flow.variable.load.indirect %182 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1305 = flow.variable.load.indirect %183 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1306 = flow.variable.load.indirect %184 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1307 = flow.variable.load.indirect %185 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1308 = flow.variable.load.indirect %186 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1309 = flow.variable.load.indirect %187 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1310 = flow.variable.load.indirect %188 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1311 = flow.variable.load.indirect %189 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1312 = flow.variable.load.indirect %190 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1313 = flow.variable.load.indirect %191 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1314 = flow.variable.load.indirect %192 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1315 = flow.variable.load.indirect %193 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1316 = flow.variable.load.indirect %194 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1317 = flow.variable.load.indirect %195 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1318 = flow.variable.load.indirect %196 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1319 = flow.variable.load.indirect %197 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1320 = flow.variable.load.indirect %198 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1321 = flow.variable.load.indirect %199 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1322 = flow.variable.load.indirect %200 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1323 = flow.variable.load.indirect %201 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1324 = flow.variable.load.indirect %202 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1325 = flow.variable.load.indirect %203 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1326 = flow.variable.load.indirect %204 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1327 = flow.variable.load.indirect %205 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1328 = flow.variable.load.indirect %206 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1329 = flow.variable.load.indirect %207 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1330 = flow.variable.load.indirect %208 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1331 = flow.variable.load.indirect %209 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1332 = flow.variable.load.indirect %210 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1333 = flow.variable.load.indirect %211 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1334 = flow.variable.load.indirect %212 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1335 = flow.variable.load.indirect %213 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1336 = flow.variable.load.indirect %214 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1337 = flow.variable.load.indirect %215 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1338 = flow.variable.load.indirect %216 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1339 = flow.variable.load.indirect %217 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1340 = flow.variable.load.indirect %218 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1341 = flow.variable.load.indirect %219 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1342 = flow.variable.load.indirect %220 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1343 = flow.variable.load.indirect %221 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1344 = flow.variable.load.indirect %222 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1345 = flow.variable.load.indirect %223 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1346 = flow.variable.load.indirect %224 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1347 = flow.variable.load.indirect %225 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1348 = flow.variable.load.indirect %226 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1349 = flow.variable.load.indirect %227 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1350 = flow.variable.load.indirect %228 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1351 = flow.variable.load.indirect %229 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1352 = flow.variable.load.indirect %230 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1353 = flow.variable.load.indirect %231 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1354 = flow.variable.load.indirect %232 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1355 = flow.variable.load.indirect %233 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1356 = flow.variable.load.indirect %234 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1357 = flow.variable.load.indirect %235 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1358 = flow.variable.load.indirect %236 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1359 = flow.variable.load.indirect %237 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1360 = flow.variable.load.indirect %238 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1361 = flow.variable.load.indirect %239 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1362 = flow.variable.load.indirect %240 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1363 = flow.variable.load.indirect %241 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1364 = flow.variable.load.indirect %242 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1365 = flow.variable.load.indirect %243 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1366 = flow.variable.load.indirect %244 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1367 = flow.variable.load.indirect %245 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1368 = flow.variable.load.indirect %246 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1369 = flow.variable.load.indirect %247 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1370 = flow.variable.load.indirect %248 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1371 = flow.variable.load.indirect %249 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1372 = flow.variable.load.indirect %250 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1373 = flow.variable.load.indirect %251 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1374 = flow.variable.load.indirect %252 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1375 = flow.variable.load.indirect %253 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1376 = flow.variable.load.indirect %254 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1377 = flow.variable.load.indirect %255 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1378 = flow.variable.load.indirect %256 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1379 = flow.variable.load.indirect %257 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1380 = flow.variable.load.indirect %258 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1381 = flow.variable.load.indirect %259 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1382 = flow.variable.load.indirect %260 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1383 = flow.variable.load.indirect %261 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1384 = flow.variable.load.indirect %262 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1385 = flow.variable.load.indirect %263 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1386 = flow.variable.load.indirect %264 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1387 = flow.variable.load.indirect %265 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1388 = flow.variable.load.indirect %266 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1389 = flow.variable.load.indirect %267 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1390 = flow.variable.load.indirect %268 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1391 = flow.variable.load.indirect %269 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1392 = flow.variable.load.indirect %270 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1393 = flow.variable.load.indirect %271 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1394 = flow.variable.load.indirect %272 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1395 = flow.variable.load.indirect %273 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1396 = flow.variable.load.indirect %274 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1397 = flow.variable.load.indirect %275 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1398 = flow.variable.load.indirect %276 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1399 = flow.variable.load.indirect %277 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1400 = flow.variable.load.indirect %278 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1401 = flow.variable.load.indirect %279 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1402 = flow.variable.load.indirect %280 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1403 = flow.variable.load.indirect %281 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1404 = flow.variable.load.indirect %282 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1405 = flow.variable.load.indirect %283 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1406 = flow.variable.load.indirect %284 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1407 = flow.variable.load.indirect %285 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1408 = flow.variable.load.indirect %286 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1409 = flow.variable.load.indirect %287 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1410 = flow.variable.load.indirect %288 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1411 = flow.variable.load.indirect %289 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1412 = flow.variable.load.indirect %290 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1413 = flow.variable.load.indirect %291 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1414 = flow.variable.load.indirect %292 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1415 = flow.variable.load.indirect %293 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1416 = flow.variable.load.indirect %294 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1417 = flow.variable.load.indirect %295 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1418 = flow.variable.load.indirect %296 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1419 = flow.variable.load.indirect %297 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1420 = flow.variable.load.indirect %298 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1421 = flow.variable.load.indirect %299 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1422 = flow.variable.load.indirect %300 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1423 = flow.variable.load.indirect %301 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1424 = flow.variable.load.indirect %302 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1425 = flow.variable.load.indirect %303 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1426 = flow.variable.load.indirect %304 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1427 = flow.variable.load.indirect %305 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1428 = flow.variable.load.indirect %306 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1429 = flow.variable.load.indirect %307 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1430 = flow.variable.load.indirect %308 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1431 = flow.variable.load.indirect %309 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1432 = flow.variable.load.indirect %310 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1433 = flow.variable.load.indirect %311 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1434 = flow.variable.load.indirect %312 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1435 = flow.variable.load.indirect %313 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1436 = flow.variable.load.indirect %314 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1437 = flow.variable.load.indirect %315 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1438 = flow.variable.load.indirect %316 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1439 = flow.variable.load.indirect %317 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1440 = flow.variable.load.indirect %318 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1441 = flow.variable.load.indirect %319 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1442 = flow.variable.load.indirect %320 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1443 = flow.variable.load.indirect %321 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1444 = flow.variable.load.indirect %322 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1445 = flow.variable.load.indirect %323 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1446 = flow.variable.load.indirect %324 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1447 = flow.variable.load.indirect %325 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1448 = flow.variable.load.indirect %326 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1449 = flow.variable.load.indirect %327 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1450 = flow.variable.load.indirect %328 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1451 = flow.variable.load.indirect %329 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1452 = flow.variable.load.indirect %330 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1453 = flow.variable.load.indirect %331 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1454 = flow.variable.load.indirect %332 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1455 = flow.variable.load.indirect %333 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1456 = flow.variable.load.indirect %334 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1457 = flow.variable.load.indirect %335 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1458 = flow.variable.load.indirect %336 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1459 = flow.variable.load.indirect %337 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1460 = flow.variable.load.indirect %338 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1461 = flow.variable.load.indirect %339 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1462 = flow.variable.load.indirect %340 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1463 = flow.variable.load.indirect %341 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1464 = flow.variable.load.indirect %342 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1465 = flow.variable.load.indirect %343 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1466 = flow.variable.load.indirect %344 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1467 = flow.variable.load.indirect %345 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1468 = flow.variable.load.indirect %346 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1469 = flow.variable.load.indirect %347 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1470 = flow.variable.load.indirect %348 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1471 = flow.variable.load.indirect %349 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1472 = flow.variable.load.indirect %350 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1473 = flow.variable.load.indirect %351 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1474 = flow.variable.load.indirect %352 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1475 = flow.variable.load.indirect %353 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1476 = flow.variable.load.indirect %354 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1477 = flow.variable.load.indirect %355 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1478 = flow.variable.load.indirect %356 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1479 = flow.variable.load.indirect %357 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1480 = flow.variable.load.indirect %358 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1481 = flow.variable.load.indirect %359 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1482 = flow.variable.load.indirect %360 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1483 = flow.variable.load.indirect %361 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1484 = flow.variable.load.indirect %362 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1485 = flow.variable.load.indirect %363 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1486 = flow.variable.load.indirect %364 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1487 = flow.variable.load.indirect %365 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1488 = flow.variable.load.indirect %366 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1489 = flow.variable.load.indirect %367 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1490 = flow.variable.load.indirect %368 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1491 = flow.variable.load.indirect %369 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1492 = flow.variable.load.indirect %370 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1493 = flow.variable.load.indirect %371 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1494 = flow.variable.load.indirect %372 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1495 = flow.variable.load.indirect %373 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1496 = flow.variable.load.indirect %374 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1497 = flow.variable.load.indirect %375 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1498 = flow.variable.load.indirect %376 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1499 = flow.variable.load.indirect %377 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1500 = flow.variable.load.indirect %378 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1501 = flow.variable.load.indirect %379 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1502 = flow.variable.load.indirect %380 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1503 = flow.variable.load.indirect %381 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1504 = flow.variable.load.indirect %382 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1505 = flow.variable.load.indirect %383 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1506 = flow.variable.load.indirect %384 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1507 = flow.variable.load.indirect %385 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1508 = flow.variable.load.indirect %386 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1509 = flow.variable.load.indirect %387 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1510 = flow.variable.load.indirect %388 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1511 = flow.variable.load.indirect %389 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1512 = flow.variable.load.indirect %390 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1513 = flow.variable.load.indirect %391 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1514 = flow.variable.load.indirect %392 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1515 = flow.variable.load.indirect %393 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1516 = flow.variable.load.indirect %394 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1517 = flow.variable.load.indirect %395 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1518 = flow.variable.load.indirect %396 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1519 = flow.variable.load.indirect %397 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1520 = flow.variable.load.indirect %398 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1521 = flow.variable.load.indirect %399 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1522 = flow.variable.load.indirect %400 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1523 = flow.variable.load.indirect %401 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1524 = flow.variable.load.indirect %402 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1525 = flow.variable.load.indirect %403 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1526 = flow.variable.load.indirect %404 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1527 = flow.variable.load.indirect %405 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1528 = flow.variable.load.indirect %406 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1529 = flow.variable.load.indirect %407 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1530 = flow.variable.load.indirect %408 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1531 = flow.variable.load.indirect %409 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1532 = flow.variable.load.indirect %410 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1533 = flow.variable.load.indirect %411 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1534 = flow.variable.load.indirect %412 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1535 = flow.variable.load.indirect %413 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1536 = flow.variable.load.indirect %414 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1537 = flow.variable.load.indirect %415 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1538 = flow.variable.load.indirect %416 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1539 = flow.variable.load.indirect %417 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1540 = flow.variable.load.indirect %418 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1541 = flow.variable.load.indirect %419 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1542 = flow.variable.load.indirect %420 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1543 = flow.variable.load.indirect %421 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1544 = flow.variable.load.indirect %422 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1545 = flow.variable.load.indirect %423 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1546 = flow.variable.load.indirect %424 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1547 = flow.variable.load.indirect %425 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1548 = flow.variable.load.indirect %426 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1549 = flow.variable.load.indirect %427 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1550 = flow.variable.load.indirect %428 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1551 = flow.variable.load.indirect %429 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1552 = flow.variable.load.indirect %430 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1553 = flow.variable.load.indirect %431 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1554 = flow.variable.load.indirect %432 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1555 = flow.variable.load.indirect %433 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1556 = flow.variable.load.indirect %434 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1557 = flow.variable.load.indirect %435 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1558 = flow.variable.load.indirect %436 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1559 = flow.variable.load.indirect %437 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1560 = flow.variable.load.indirect %438 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1561 = flow.variable.load.indirect %439 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1562 = flow.variable.load.indirect %440 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1563 = flow.variable.load.indirect %441 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1564 = flow.variable.load.indirect %442 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1565 = flow.variable.load.indirect %443 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1566 = flow.variable.load.indirect %444 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1567 = flow.variable.load.indirect %445 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1568 = flow.variable.load.indirect %446 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1569 = flow.variable.load.indirect %447 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1570 = flow.variable.load.indirect %448 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1571 = flow.variable.load.indirect %449 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1572 = flow.variable.load.indirect %450 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1573 = flow.variable.load.indirect %451 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1574 = flow.variable.load.indirect %452 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1575 = flow.variable.load.indirect %453 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1576 = flow.variable.load.indirect %454 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1577 = flow.variable.load.indirect %455 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1578 = flow.variable.load.indirect %456 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1579 = flow.variable.load.indirect %457 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1580 = flow.variable.load.indirect %458 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1581 = flow.variable.load.indirect %459 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1582 = flow.variable.load.indirect %460 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1583 = flow.variable.load.indirect %461 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1584 = flow.variable.load.indirect %462 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1585 = flow.variable.load.indirect %463 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1586 = flow.variable.load.indirect %464 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1587 = flow.variable.load.indirect %465 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1588 = flow.variable.load.indirect %466 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1589 = flow.variable.load.indirect %467 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1590 = flow.variable.load.indirect %468 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1591 = flow.variable.load.indirect %469 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1592 = flow.variable.load.indirect %470 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1593 = flow.variable.load.indirect %471 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1594 = flow.variable.load.indirect %472 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1595 = flow.variable.load.indirect %473 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1596 = flow.variable.load.indirect %474 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1597 = flow.variable.load.indirect %475 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1598 = flow.variable.load.indirect %476 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1599 = flow.variable.load.indirect %477 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1600 = flow.variable.load.indirect %478 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1601 = flow.variable.load.indirect %479 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1602 = flow.variable.load.indirect %480 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1603 = flow.variable.load.indirect %481 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1604 = flow.variable.load.indirect %482 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1605 = flow.variable.load.indirect %483 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1606 = flow.variable.load.indirect %484 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1607 = flow.variable.load.indirect %485 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1608 = flow.variable.load.indirect %486 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1609 = flow.variable.load.indirect %487 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1610 = flow.variable.load.indirect %488 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1611 = flow.variable.load.indirect %489 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1612 = flow.variable.load.indirect %490 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1613 = flow.variable.load.indirect %491 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1614 = flow.variable.load.indirect %492 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1615 = flow.variable.load.indirect %493 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1616 = flow.variable.load.indirect %494 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1617 = flow.variable.load.indirect %495 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1618 = flow.variable.load.indirect %496 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1619 = flow.variable.load.indirect %497 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1620 = flow.variable.load.indirect %498 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1621 = flow.variable.load.indirect %499 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1622 = flow.variable.load.indirect %500 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1623 = flow.variable.load.indirect %501 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1624 = flow.variable.load.indirect %502 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1625 = flow.variable.load.indirect %503 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1626 = flow.variable.load.indirect %504 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1627 = flow.variable.load.indirect %505 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1628 = flow.variable.load.indirect %506 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1629 = flow.variable.load.indirect %507 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1630 = flow.variable.load.indirect %508 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1631 = flow.variable.load.indirect %509 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1632 = flow.variable.load.indirect %510 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1633 = flow.variable.load.indirect %511 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1634 = flow.variable.load.indirect %512 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1635 = flow.variable.load.indirect %513 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1636 = flow.variable.load.indirect %514 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1637 = flow.variable.load.indirect %515 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1638 = flow.variable.load.indirect %516 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1639 = flow.variable.load.indirect %517 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1640 = flow.variable.load.indirect %518 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1641 = flow.variable.load.indirect %519 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1642 = flow.variable.load.indirect %520 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1643 = flow.variable.load.indirect %521 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1644 = flow.variable.load.indirect %522 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1645 = flow.variable.load.indirect %523 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1646 = flow.variable.load.indirect %524 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1647 = flow.variable.load.indirect %525 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1648 = flow.variable.load.indirect %526 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1649 = flow.variable.load.indirect %527 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1650 = flow.variable.load.indirect %528 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1651 = flow.variable.load.indirect %529 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1652 = flow.variable.load.indirect %530 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1653 = flow.variable.load.indirect %531 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1654 = flow.variable.load.indirect %532 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1655 = flow.variable.load.indirect %533 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1656 = flow.variable.load.indirect %534 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1657 = flow.variable.load.indirect %535 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1658 = flow.variable.load.indirect %536 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1659 = flow.variable.load.indirect %537 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1660 = flow.variable.load.indirect %538 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1661 = flow.variable.load.indirect %539 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1662 = flow.variable.load.indirect %540 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1663 = flow.variable.load.indirect %541 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1664 = flow.variable.load.indirect %542 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1665 = flow.variable.load.indirect %543 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1666 = flow.variable.load.indirect %544 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1667 = flow.variable.load.indirect %545 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1668 = flow.variable.load.indirect %546 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1669 = flow.variable.load.indirect %547 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1670 = flow.variable.load.indirect %548 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1671 = flow.variable.load.indirect %549 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1672 = flow.variable.load.indirect %550 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1673 = flow.variable.load.indirect %551 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1674 = flow.variable.load.indirect %552 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1675 = flow.variable.load.indirect %553 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1676 = flow.variable.load.indirect %554 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1677 = flow.variable.load.indirect %555 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1678 = flow.variable.load.indirect %556 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1679 = flow.variable.load.indirect %557 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1680 = flow.variable.load.indirect %558 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1681 = flow.variable.load.indirect %559 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1682 = flow.variable.load.indirect %560 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1683 = flow.variable.load.indirect %561 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1684 = flow.variable.load.indirect %562 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1685 = flow.variable.load.indirect %563 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1686 = flow.variable.load.indirect %564 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1687 = flow.variable.load.indirect %565 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1688 = flow.variable.load.indirect %566 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1689 = flow.variable.load.indirect %567 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1690 = flow.variable.load.indirect %568 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1691 = flow.variable.load.indirect %569 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1692 = flow.variable.load.indirect %570 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1693 = flow.variable.load.indirect %571 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1694 = flow.variable.load.indirect %572 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1695 = flow.variable.load.indirect %573 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1696 = flow.variable.load.indirect %574 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1697 = flow.variable.load.indirect %575 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1698 = flow.variable.load.indirect %576 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1699 = flow.variable.load.indirect %577 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1700 = flow.variable.load.indirect %578 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1701 = flow.variable.load.indirect %579 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1702 = flow.variable.load.indirect %580 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1703 = flow.variable.load.indirect %581 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1704 = flow.variable.load.indirect %582 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1705 = flow.variable.load.indirect %583 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1706 = flow.variable.load.indirect %584 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1707 = flow.variable.load.indirect %585 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1708 = flow.variable.load.indirect %586 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1709 = flow.variable.load.indirect %587 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1710 = flow.variable.load.indirect %588 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1711 = flow.variable.load.indirect %589 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1712 = flow.variable.load.indirect %590 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1713 = flow.variable.load.indirect %591 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1714 = flow.variable.load.indirect %592 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1715 = flow.variable.load.indirect %593 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1716 = flow.variable.load.indirect %594 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1717 = flow.variable.load.indirect %595 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1718 = flow.variable.load.indirect %596 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1719 = flow.variable.load.indirect %597 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1720 = flow.variable.load.indirect %598 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1721 = flow.variable.load.indirect %599 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1722 = flow.variable.load.indirect %600 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1723 = flow.variable.load.indirect %601 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1724 = flow.variable.load.indirect %602 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1725 = flow.variable.load.indirect %603 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1726 = flow.variable.load.indirect %604 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1727 = flow.variable.load.indirect %605 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1728 = flow.variable.load.indirect %606 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1729 = flow.variable.load.indirect %607 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1730 = flow.variable.load.indirect %608 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1731 = flow.variable.load.indirect %609 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1732 = flow.variable.load.indirect %610 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1733 = flow.variable.load.indirect %611 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1734 = flow.variable.load.indirect %612 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1735 = flow.variable.load.indirect %613 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1736 = flow.variable.load.indirect %614 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1737 = flow.variable.load.indirect %615 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1738 = flow.variable.load.indirect %616 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1739 = flow.variable.load.indirect %617 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1740 = flow.variable.load.indirect %618 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1741 = flow.variable.load.indirect %619 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1742 = flow.variable.load.indirect %620 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1743 = flow.variable.load.indirect %621 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1744 = flow.variable.load.indirect %622 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1745 = flow.variable.load.indirect %623 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1746 = flow.variable.load.indirect %624 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1747 = flow.variable.load.indirect %625 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1748 = flow.variable.load.indirect %626 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1749 = flow.variable.load.indirect %627 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1750 = flow.variable.load.indirect %628 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1751 = flow.variable.load.indirect %629 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1752 = flow.variable.load.indirect %630 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1753 = flow.variable.load.indirect %631 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1754 = flow.variable.load.indirect %632 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1755 = flow.variable.load.indirect %633 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1756 = flow.variable.load.indirect %634 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1757 = flow.variable.load.indirect %635 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1758 = flow.variable.load.indirect %636 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1759 = flow.variable.load.indirect %637 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1760 = flow.variable.load.indirect %638 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1761 = flow.variable.load.indirect %639 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1762 = flow.variable.load.indirect %640 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1763 = flow.variable.load.indirect %641 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1764 = flow.variable.load.indirect %642 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1765 = flow.variable.load.indirect %643 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1766 = flow.variable.load.indirect %644 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1767 = flow.variable.load.indirect %645 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1768 = flow.variable.load.indirect %646 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1769 = flow.variable.load.indirect %647 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1770 = flow.variable.load.indirect %648 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1771 = flow.variable.load.indirect %649 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1772 = flow.variable.load.indirect %650 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1773 = flow.variable.load.indirect %651 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1774 = flow.variable.load.indirect %652 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1775 = flow.variable.load.indirect %653 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1776 = flow.variable.load.indirect %654 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1777 = flow.variable.load.indirect %655 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1778 = flow.variable.load.indirect %656 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1779 = flow.variable.load.indirect %657 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1780 = flow.variable.load.indirect %658 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1781 = flow.variable.load.indirect %659 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1782 = flow.variable.load.indirect %660 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1783 = flow.variable.load.indirect %661 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1784 = flow.variable.load.indirect %662 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1785 = flow.variable.load.indirect %663 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1786 = flow.variable.load.indirect %664 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1787 = flow.variable.load.indirect %665 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1788 = flow.variable.load.indirect %666 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1789 = flow.variable.load.indirect %667 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1790 = flow.variable.load.indirect %668 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1791 = flow.variable.load.indirect %669 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1792 = flow.variable.load.indirect %670 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1793 = flow.variable.load.indirect %671 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1794 = flow.variable.load.indirect %672 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1795 = flow.variable.load.indirect %673 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1796 = flow.variable.load.indirect %674 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1797 = flow.variable.load.indirect %675 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1798 = flow.variable.load.indirect %676 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1799 = flow.variable.load.indirect %677 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1800 = flow.variable.load.indirect %678 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1801 = flow.variable.load.indirect %679 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1802 = flow.variable.load.indirect %680 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1803 = flow.variable.load.indirect %681 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1804 = flow.variable.load.indirect %682 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1805 = flow.variable.load.indirect %683 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1806 = flow.variable.load.indirect %684 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1807 = flow.variable.load.indirect %685 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1808 = flow.variable.load.indirect %686 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1809 = flow.variable.load.indirect %687 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1810 = flow.variable.load.indirect %688 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1811 = flow.variable.load.indirect %689 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1812 = flow.variable.load.indirect %690 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1813 = flow.variable.load.indirect %691 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1814 = flow.variable.load.indirect %692 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1815 = flow.variable.load.indirect %693 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1816 = flow.variable.load.indirect %694 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1817 = flow.variable.load.indirect %695 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1818 = flow.variable.load.indirect %696 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1819 = flow.variable.load.indirect %697 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1820 = flow.variable.load.indirect %698 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1821 = flow.variable.load.indirect %699 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1822 = flow.variable.load.indirect %700 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1823 = flow.variable.load.indirect %701 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1824 = flow.variable.load.indirect %702 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1825 = flow.variable.load.indirect %703 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1826 = flow.variable.load.indirect %704 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1827 = flow.variable.load.indirect %705 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1828 = flow.variable.load.indirect %706 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1829 = flow.variable.load.indirect %707 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1830 = flow.variable.load.indirect %708 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1831 = flow.variable.load.indirect %709 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1832 = flow.variable.load.indirect %710 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1833 = flow.variable.load.indirect %711 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1834 = flow.variable.load.indirect %712 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1835 = flow.variable.load.indirect %713 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1836 = flow.variable.load.indirect %714 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1837 = flow.variable.load.indirect %715 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1838 = flow.variable.load.indirect %716 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1839 = flow.variable.load.indirect %717 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1840 = flow.variable.load.indirect %718 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1841 = flow.variable.load.indirect %719 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1842 = flow.variable.load.indirect %720 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1843 = flow.variable.load.indirect %721 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1844 = flow.variable.load.indirect %722 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1845 = flow.variable.load.indirect %723 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1846 = flow.variable.load.indirect %724 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1847 = flow.variable.load.indirect %725 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1848 = flow.variable.load.indirect %726 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1849 = flow.variable.load.indirect %727 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1850 = flow.variable.load.indirect %728 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1851 = flow.variable.load.indirect %729 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1852 = flow.variable.load.indirect %730 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1853 = flow.variable.load.indirect %731 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1854 = flow.variable.load.indirect %732 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1855 = flow.variable.load.indirect %733 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1856 = flow.variable.load.indirect %734 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1857 = flow.variable.load.indirect %735 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1858 = flow.variable.load.indirect %736 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1859 = flow.variable.load.indirect %737 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1860 = flow.variable.load.indirect %738 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1861 = flow.variable.load.indirect %739 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1862 = flow.variable.load.indirect %740 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1863 = flow.variable.load.indirect %741 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1864 = flow.variable.load.indirect %742 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1865 = flow.variable.load.indirect %743 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1866 = flow.variable.load.indirect %744 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1867 = flow.variable.load.indirect %745 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1868 = flow.variable.load.indirect %746 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1869 = flow.variable.load.indirect %747 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1870 = flow.variable.load.indirect %748 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1871 = flow.variable.load.indirect %749 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1872 = flow.variable.load.indirect %750 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1873 = flow.variable.load.indirect %751 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1874 = flow.variable.load.indirect %752 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1875 = flow.variable.load.indirect %753 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1876 = flow.variable.load.indirect %754 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1877 = flow.variable.load.indirect %755 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1878 = flow.variable.load.indirect %756 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1879 = flow.variable.load.indirect %757 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1880 = flow.variable.load.indirect %758 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1881 = flow.variable.load.indirect %759 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1882 = flow.variable.load.indirect %760 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1883 = flow.variable.load.indirect %761 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1884 = flow.variable.load.indirect %762 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1885 = flow.variable.load.indirect %763 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1886 = flow.variable.load.indirect %764 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1887 = flow.variable.load.indirect %765 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1888 = flow.variable.load.indirect %766 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1889 = flow.variable.load.indirect %767 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1890 = flow.variable.load.indirect %768 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1891 = flow.variable.load.indirect %769 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1892 = flow.variable.load.indirect %770 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1893 = flow.variable.load.indirect %771 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1894 = flow.variable.load.indirect %772 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1895 = flow.variable.load.indirect %773 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1896 = flow.variable.load.indirect %774 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1897 = flow.variable.load.indirect %775 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1898 = flow.variable.load.indirect %776 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1899 = flow.variable.load.indirect %777 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1900 = flow.variable.load.indirect %778 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1901 = flow.variable.load.indirect %779 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1902 = flow.variable.load.indirect %780 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1903 = flow.variable.load.indirect %781 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1904 = flow.variable.load.indirect %782 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1905 = flow.variable.load.indirect %783 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1906 = flow.variable.load.indirect %784 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1907 = flow.variable.load.indirect %785 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1908 = flow.variable.load.indirect %786 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1909 = flow.variable.load.indirect %787 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1910 = flow.variable.load.indirect %788 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1911 = flow.variable.load.indirect %789 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1912 = flow.variable.load.indirect %790 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1913 = flow.variable.load.indirect %791 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1914 = flow.variable.load.indirect %792 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1915 = flow.variable.load.indirect %793 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1916 = flow.variable.load.indirect %794 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1917 = flow.variable.load.indirect %795 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1918 = flow.variable.load.indirect %796 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1919 = flow.variable.load.indirect %797 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1920 = flow.variable.load.indirect %798 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1921 = flow.variable.load.indirect %799 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1922 = flow.variable.load.indirect %800 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1923 = flow.variable.load.indirect %801 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1924 = flow.variable.load.indirect %802 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1925 = flow.variable.load.indirect %803 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1926 = flow.variable.load.indirect %804 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1927 = flow.variable.load.indirect %805 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1928 = flow.variable.load.indirect %806 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1929 = flow.variable.load.indirect %807 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1930 = flow.variable.load.indirect %808 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1931 = flow.variable.load.indirect %809 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1932 = flow.variable.load.indirect %810 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1933 = flow.variable.load.indirect %811 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1934 = flow.variable.load.indirect %812 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1935 = flow.variable.load.indirect %813 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1936 = flow.variable.load.indirect %814 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1937 = flow.variable.load.indirect %815 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1938 = flow.variable.load.indirect %816 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1939 = flow.variable.load.indirect %817 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1940 = flow.variable.load.indirect %818 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1941 = flow.variable.load.indirect %819 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1942 = flow.variable.load.indirect %820 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1943 = flow.variable.load.indirect %821 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1944 = flow.variable.load.indirect %822 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1945 = flow.variable.load.indirect %823 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1946 = flow.variable.load.indirect %824 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1947 = flow.variable.load.indirect %825 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1948 = flow.variable.load.indirect %826 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1949 = flow.variable.load.indirect %827 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1950 = flow.variable.load.indirect %828 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1951 = flow.variable.load.indirect %829 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1952 = flow.variable.load.indirect %830 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1953 = flow.variable.load.indirect %831 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1954 = flow.variable.load.indirect %832 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1955 = flow.variable.load.indirect %833 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1956 = flow.variable.load.indirect %834 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1957 = flow.variable.load.indirect %835 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1958 = flow.variable.load.indirect %836 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1959 = flow.variable.load.indirect %837 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1960 = flow.variable.load.indirect %838 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1961 = flow.variable.load.indirect %839 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1962 = flow.variable.load.indirect %840 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1963 = flow.variable.load.indirect %841 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1964 = flow.variable.load.indirect %842 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %1965 = flow.variable.load.indirect %843 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1966 = flow.variable.load.indirect %844 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1967 = flow.variable.load.indirect %845 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1968 = flow.variable.load.indirect %846 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1969 = flow.variable.load.indirect %847 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1970 = flow.variable.load.indirect %848 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1971 = flow.variable.load.indirect %849 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1972 = flow.variable.load.indirect %850 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1973 = flow.variable.load.indirect %851 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1974 = flow.variable.load.indirect %852 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1975 = flow.variable.load.indirect %853 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1976 = flow.variable.load.indirect %854 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1977 = flow.variable.load.indirect %855 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1978 = flow.variable.load.indirect %856 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1979 = flow.variable.load.indirect %857 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1980 = flow.variable.load.indirect %858 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1981 = flow.variable.load.indirect %859 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1982 = flow.variable.load.indirect %860 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1983 = flow.variable.load.indirect %861 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1984 = flow.variable.load.indirect %862 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1985 = flow.variable.load.indirect %863 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1986 = flow.variable.load.indirect %864 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1987 = flow.variable.load.indirect %865 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1988 = flow.variable.load.indirect %866 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1989 = flow.variable.load.indirect %867 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1990 = flow.variable.load.indirect %868 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1991 = flow.variable.load.indirect %869 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1992 = flow.variable.load.indirect %870 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %1993 = flow.variable.load.indirect %871 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1994 = flow.variable.load.indirect %872 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %1995 = flow.variable.load.indirect %873 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1996 = flow.variable.load.indirect %874 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %1997 = flow.variable.load.indirect %875 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1998 = flow.variable.load.indirect %876 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %1999 = flow.variable.load.indirect %877 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2000 = flow.variable.load.indirect %878 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2001 = flow.variable.load.indirect %879 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2002 = flow.variable.load.indirect %880 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2003 = flow.variable.load.indirect %881 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2004 = flow.variable.load.indirect %882 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2005 = flow.variable.load.indirect %883 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2006 = flow.variable.load.indirect %884 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2007 = flow.variable.load.indirect %885 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2008 = flow.variable.load.indirect %886 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2009 = flow.variable.load.indirect %887 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2010 = flow.variable.load.indirect %888 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2011 = flow.variable.load.indirect %889 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2012 = flow.variable.load.indirect %890 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2013 = flow.variable.load.indirect %891 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2014 = flow.variable.load.indirect %892 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2015 = flow.variable.load.indirect %893 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2016 = flow.variable.load.indirect %894 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2017 = flow.variable.load.indirect %895 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2018 = flow.variable.load.indirect %896 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2019 = flow.variable.load.indirect %897 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2020 = flow.variable.load.indirect %898 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2021 = flow.variable.load.indirect %899 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2022 = flow.variable.load.indirect %900 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2023 = flow.variable.load.indirect %901 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2024 = flow.variable.load.indirect %902 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2025 = flow.variable.load.indirect %903 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2026 = flow.variable.load.indirect %904 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2027 = flow.variable.load.indirect %905 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2028 = flow.variable.load.indirect %906 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2029 = flow.variable.load.indirect %907 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2030 = flow.variable.load.indirect %908 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2031 = flow.variable.load.indirect %909 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2032 = flow.variable.load.indirect %910 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2033 = flow.variable.load.indirect %911 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2034 = flow.variable.load.indirect %912 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2035 = flow.variable.load.indirect %913 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2036 = flow.variable.load.indirect %914 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2037 = flow.variable.load.indirect %915 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2038 = flow.variable.load.indirect %916 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2039 = flow.variable.load.indirect %917 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2040 = flow.variable.load.indirect %918 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2041 = flow.variable.load.indirect %919 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2042 = flow.variable.load.indirect %920 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2043 = flow.variable.load.indirect %921 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2044 = flow.variable.load.indirect %922 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2045 = flow.variable.load.indirect %923 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2046 = flow.variable.load.indirect %924 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2047 = flow.variable.load.indirect %925 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2048 = flow.variable.load.indirect %926 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2049 = flow.variable.load.indirect %927 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2050 = flow.variable.load.indirect %928 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2051 = flow.variable.load.indirect %929 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2052 = flow.variable.load.indirect %930 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2053 = flow.variable.load.indirect %931 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2054 = flow.variable.load.indirect %932 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2055 = flow.variable.load.indirect %933 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2056 = flow.variable.load.indirect %934 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2057 = flow.variable.load.indirect %935 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2058 = flow.variable.load.indirect %936 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2059 = flow.variable.load.indirect %937 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2060 = flow.variable.load.indirect %938 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2061 = flow.variable.load.indirect %939 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2062 = flow.variable.load.indirect %940 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2063 = flow.variable.load.indirect %941 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2064 = flow.variable.load.indirect %942 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2065 = flow.variable.load.indirect %943 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2066 = flow.variable.load.indirect %944 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2067 = flow.variable.load.indirect %945 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2068 = flow.variable.load.indirect %946 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2069 = flow.variable.load.indirect %947 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2070 = flow.variable.load.indirect %948 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2071 = flow.variable.load.indirect %949 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2072 = flow.variable.load.indirect %950 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2073 = flow.variable.load.indirect %951 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2074 = flow.variable.load.indirect %952 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2075 = flow.variable.load.indirect %953 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2076 = flow.variable.load.indirect %954 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2077 = flow.variable.load.indirect %955 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2078 = flow.variable.load.indirect %956 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2079 = flow.variable.load.indirect %957 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2080 = flow.variable.load.indirect %958 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2081 = flow.variable.load.indirect %959 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2082 = flow.variable.load.indirect %960 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2083 = flow.variable.load.indirect %961 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2084 = flow.variable.load.indirect %962 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2085 = flow.variable.load.indirect %963 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2086 = flow.variable.load.indirect %964 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2087 = flow.variable.load.indirect %965 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2088 = flow.variable.load.indirect %966 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2089 = flow.variable.load.indirect %967 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2090 = flow.variable.load.indirect %968 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2091 = flow.variable.load.indirect %969 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2092 = flow.variable.load.indirect %970 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2093 = flow.variable.load.indirect %971 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2094 = flow.variable.load.indirect %972 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2095 = flow.variable.load.indirect %973 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2096 = flow.variable.load.indirect %974 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2097 = flow.variable.load.indirect %975 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2098 = flow.variable.load.indirect %976 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2099 = flow.variable.load.indirect %977 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2100 = flow.variable.load.indirect %978 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2101 = flow.variable.load.indirect %979 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2102 = flow.variable.load.indirect %980 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2103 = flow.variable.load.indirect %981 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2104 = flow.variable.load.indirect %982 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2105 = flow.variable.load.indirect %983 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2106 = flow.variable.load.indirect %984 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2107 = flow.variable.load.indirect %985 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2108 = flow.variable.load.indirect %986 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2109 = flow.variable.load.indirect %987 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2110 = flow.variable.load.indirect %988 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2111 = flow.variable.load.indirect %989 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2112 = flow.variable.load.indirect %990 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2113 = flow.variable.load.indirect %991 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2114 = flow.variable.load.indirect %992 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2115 = flow.variable.load.indirect %993 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2116 = flow.variable.load.indirect %994 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2117 = flow.variable.load.indirect %995 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2118 = flow.variable.load.indirect %996 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2119 = flow.variable.load.indirect %997 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2120 = flow.variable.load.indirect %998 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2121 = flow.variable.load.indirect %999 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2122 = flow.variable.load.indirect %1000 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2123 = flow.variable.load.indirect %1001 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2124 = flow.variable.load.indirect %1002 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2125 = flow.variable.load.indirect %1003 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2126 = flow.variable.load.indirect %1004 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2127 = flow.variable.load.indirect %1005 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2128 = flow.variable.load.indirect %1006 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2129 = flow.variable.load.indirect %1007 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2130 = flow.variable.load.indirect %1008 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2131 = flow.variable.load.indirect %1009 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2132 = flow.variable.load.indirect %1010 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2133 = flow.variable.load.indirect %1011 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2134 = flow.variable.load.indirect %1012 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2135 = flow.variable.load.indirect %1013 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2136 = flow.variable.load.indirect %1014 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2137 = flow.variable.load.indirect %1015 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2138 = flow.variable.load.indirect %1016 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2139 = flow.variable.load.indirect %1017 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2140 = flow.variable.load.indirect %1018 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2141 = flow.variable.load.indirect %1019 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2142 = flow.variable.load.indirect %1020 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2143 = flow.variable.load.indirect %1021 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2144 = flow.variable.load.indirect %1022 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2145 = flow.variable.load.indirect %1023 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2146 = flow.variable.load.indirect %1024 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2147 = flow.variable.load.indirect %1025 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2148 = flow.variable.load.indirect %1026 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2149 = flow.variable.load.indirect %1027 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2150 = flow.variable.load.indirect %1028 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2151 = flow.variable.load.indirect %1029 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2152 = flow.variable.load.indirect %1030 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2153 = flow.variable.load.indirect %1031 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2154 = flow.variable.load.indirect %1032 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2155 = flow.variable.load.indirect %1033 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2156 = flow.variable.load.indirect %1034 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2157 = flow.variable.load.indirect %1035 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2158 = flow.variable.load.indirect %1036 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2159 = flow.variable.load.indirect %1037 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2160 = flow.variable.load.indirect %1038 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2161 = flow.variable.load.indirect %1039 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2162 = flow.variable.load.indirect %1040 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2163 = flow.variable.load.indirect %1041 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2164 = flow.variable.load.indirect %1042 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2165 = flow.variable.load.indirect %1043 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2166 = flow.variable.load.indirect %1044 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2167 = flow.variable.load.indirect %1045 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2168 = flow.variable.load.indirect %1046 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2169 = flow.variable.load.indirect %1047 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2170 = flow.variable.load.indirect %1048 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2171 = flow.variable.load.indirect %1049 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2172 = flow.variable.load.indirect %1050 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2173 = flow.variable.load.indirect %1051 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2174 = flow.variable.load.indirect %1052 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2175 = flow.variable.load.indirect %1053 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2176 = flow.variable.load.indirect %1054 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2177 = flow.variable.load.indirect %1055 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2178 = flow.variable.load.indirect %1056 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2179 = flow.variable.load.indirect %1057 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2180 = flow.variable.load.indirect %1058 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2181 = flow.variable.load.indirect %1059 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2182 = flow.variable.load.indirect %1060 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2183 = flow.variable.load.indirect %1061 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2184 = flow.variable.load.indirect %1062 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2185 = flow.variable.load.indirect %1063 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2186 = flow.variable.load.indirect %1064 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2187 = flow.variable.load.indirect %1065 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2188 = flow.variable.load.indirect %1066 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2189 = flow.variable.load.indirect %1067 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2190 = flow.variable.load.indirect %1068 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2191 = flow.variable.load.indirect %1069 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2192 = flow.variable.load.indirect %1070 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2193 = flow.variable.load.indirect %1071 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2194 = flow.variable.load.indirect %1072 : !iree.ptr<tensor<128x128xf32>> -> tensor<128x128xf32>
+ %2195 = flow.variable.load.indirect %1073 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2196 = flow.variable.load.indirect %1074 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2197 = flow.variable.load.indirect %1075 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2198 = flow.variable.load.indirect %1076 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2199 = flow.variable.load.indirect %1077 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2200 = flow.variable.load.indirect %1078 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2201 = flow.variable.load.indirect %1079 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2202 = flow.variable.load.indirect %1080 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2203 = flow.variable.load.indirect %1081 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2204 = flow.variable.load.indirect %1082 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2205 = flow.variable.load.indirect %1083 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2206 = flow.variable.load.indirect %1084 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2207 = flow.variable.load.indirect %1085 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2208 = flow.variable.load.indirect %1086 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2209 = flow.variable.load.indirect %1087 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2210 = flow.variable.load.indirect %1088 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2211 = flow.variable.load.indirect %1089 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2212 = flow.variable.load.indirect %1090 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2213 = flow.variable.load.indirect %1091 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2214 = flow.variable.load.indirect %1092 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2215 = flow.variable.load.indirect %1093 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2216 = flow.variable.load.indirect %1094 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2217 = flow.variable.load.indirect %1095 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2218 = flow.variable.load.indirect %1096 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2219 = flow.variable.load.indirect %1097 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2220 = flow.variable.load.indirect %1098 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2221 = flow.variable.load.indirect %1099 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2222 = flow.variable.load.indirect %1100 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2223 = flow.variable.load.indirect %1101 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2224 = flow.variable.load.indirect %1102 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2225 = flow.variable.load.indirect %1103 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2226 = flow.variable.load.indirect %1104 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2227 = flow.variable.load.indirect %1105 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2228 = flow.variable.load.indirect %1106 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2229 = flow.variable.load.indirect %1107 : !iree.ptr<tensor<512xf32>> -> tensor<512xf32>
+ %2230 = flow.variable.load.indirect %1108 : !iree.ptr<tensor<128x512xf32>> -> tensor<128x512xf32>
+ %2231 = flow.variable.load.indirect %1109 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>
+ %2232 = flow.variable.load.indirect %1110 : !iree.ptr<tensor<512x128xf32>> -> tensor<512x128xf32>
+ %2233 = flow.variable.load.indirect %1111 : !iree.ptr<tensor<2xf32>> -> tensor<2xf32>
+ %2234 = flow.variable.load.indirect %1112 : !iree.ptr<tensor<2x512xf32>> -> tensor<2x512xf32>
+ %2235 = "mhlo.reshape"(%arg1) : (tensor<1x384xi32>) -> tensor<1x384x1xi32>
+ %2236 = "mhlo.torch_index_select"(%1128, %2235) {batch_dims = 0 : i64, dim = 0 : i64} : (tensor<30522x128xf32>, tensor<1x384x1xi32>) -> tensor<1x384x1x128xf32>
+ %2237 = "mhlo.reshape"(%2236) : (tensor<1x384x1x128xf32>) -> tensor<1x384x128xf32>
+ %2238 = "mhlo.slice"(%2237) {limit_indices = dense<[1, 384, 128]> : tensor<3xi64>, start_indices = dense<[0, 1, 0]> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} : (tensor<1x384x128xf32>) -> tensor<1x383x128xf32>
+ %2239 = "mhlo.pad"(%2238, %1118) {edge_padding_high = dense<[0, 1, 0]> : tensor<3xi64>, edge_padding_low = dense<0> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>} : (tensor<1x383x128xf32>, tensor<f32>) -> tensor<1x384x128xf32>
+ %2240 = "mhlo.slice"(%2237) {limit_indices = dense<[1, 383, 128]> : tensor<3xi64>, start_indices = dense<0> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} : (tensor<1x384x128xf32>) -> tensor<1x383x128xf32>
+ %2241 = "mhlo.pad"(%2240, %1118) {edge_padding_high = dense<0> : tensor<3xi64>, edge_padding_low = dense<[0, 1, 0]> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>} : (tensor<1x383x128xf32>, tensor<f32>) -> tensor<1x384x128xf32>
+ %2242 = "mhlo.concatenate"(%2239, %2237, %2241) {dimension = 2 : i64} : (tensor<1x384x128xf32>, tensor<1x384x128xf32>, tensor<1x384x128xf32>) -> tensor<1x384x384xf32>
+ %2243 = "mhlo.reshape"(%2242) : (tensor<1x384x384xf32>) -> tensor<384x384xf32>
+ %2244 = "mhlo.dot"(%2243, %1123) : (tensor<384x384xf32>, tensor<384x512xf32>) -> tensor<384x512xf32>
+ %2245 = "mhlo.broadcast_in_dim"(%1122) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2246 = mhlo.add %2244, %2245 : tensor<384x512xf32>
+ %2247 = "mhlo.reshape"(%2246) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2248 = "mhlo.convert"(%arg0) : (tensor<1x384xi32>) -> tensor<1x384xf32>
+ %2249 = "mhlo.reshape"(%2248) : (tensor<1x384xf32>) -> tensor<1x1x384xf32>
+ %2250 = "mhlo.broadcast_in_dim"(%2249) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x1x384xf32>) -> tensor<1x384x384xf32>
+ %2251 = mhlo.multiply %2250, %1116 : tensor<1x384x384xf32>
+ %2252 = "mhlo.reshape"(%2251) : (tensor<1x384x384xf32>) -> tensor<1x1x384x384xf32>
+ %2253 = mhlo.multiply %2252, %1115 : tensor<1x1x384x384xf32>
+ %2254 = mhlo.add %2253, %1113 : tensor<1x1x384x384xf32>
+ %2255 = "mhlo.torch_index_select"(%1127, %arg2) {batch_dims = 0 : i64, dim = 0 : i64} : (tensor<2x512xf32>, tensor<1x384xi32>) -> tensor<1x384x512xf32>
+ %2256 = mhlo.add %2247, %2255 : tensor<1x384x512xf32>
+ %2257 = mhlo.add %2256, %1126 : tensor<1x384x512xf32>
+ %2258 = "mhlo.broadcast_in_dim"(%1121) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2259 = mhlo.multiply %2257, %2258 : tensor<1x384x512xf32>
+ %2260 = "mhlo.broadcast_in_dim"(%1120) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2261 = mhlo.add %2259, %2260 : tensor<1x384x512xf32>
+ %2262 = "mhlo.reshape"(%2261) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2263 = "mhlo.dot"(%2262, %1138) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2264 = "mhlo.broadcast_in_dim"(%1137) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2265 = mhlo.add %2263, %2264 : tensor<384x128xf32>
+ %2266 = "mhlo.reshape"(%2265) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2267 = "mhlo.transpose"(%2266) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2268 = "mhlo.dot"(%2262, %1142) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2269 = "mhlo.reshape"(%2268) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2270 = "mhlo.broadcast_in_dim"(%1141) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2271 = mhlo.add %2269, %2270 : tensor<1x384x128xf32>
+ %2272 = "mhlo.broadcast_in_dim"(%1140) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2273 = mhlo.multiply %2271, %2272 : tensor<1x384x128xf32>
+ %2274 = "mhlo.broadcast_in_dim"(%1139) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2275 = mhlo.add %2273, %2274 : tensor<1x384x128xf32>
+ %2276 = "mhlo.reshape"(%2275) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2277 = "mhlo.dot"(%2276, %1134) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2278 = "mhlo.broadcast_in_dim"(%1133) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2279 = mhlo.add %2277, %2278 : tensor<384x128xf32>
+ %2280 = "mhlo.reshape"(%2279) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2281 = "mhlo.transpose"(%2280) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2282 = "mhlo.dot"(%2276, %1136) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2283 = "mhlo.broadcast_in_dim"(%1135) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2284 = mhlo.add %2282, %2283 : tensor<384x128xf32>
+ %2285 = "mhlo.reshape"(%2284) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2286 = "mhlo.transpose"(%2285) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2287 = "mhlo.dot_general"(%2286, %2281) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2288 = mhlo.multiply %2287, %1114 : tensor<1x4x384x384xf32>
+ %2289 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2290 = mhlo.add %2288, %2289 : tensor<1x4x384x384xf32>
+ %2291 = "mhlo.reduce"(%2290, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2292 = "mhlo.broadcast_in_dim"(%2291) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2293 = mhlo.subtract %2290, %2292 : tensor<1x4x384x384xf32>
+ %2294 = "mhlo.exponential"(%2293) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2295 = "mhlo.reduce"(%2294, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2296 = "mhlo.broadcast_in_dim"(%2295) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2297 = mhlo.divide %2294, %2296 : tensor<1x4x384x384xf32>
+ %2298 = "mhlo.dot_general"(%2297, %2267) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2299 = "mhlo.transpose"(%2298) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2300 = "mhlo.reshape"(%2299) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2301 = "mhlo.dot"(%2300, %1132) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2302 = "mhlo.broadcast_in_dim"(%1131) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2303 = mhlo.add %2301, %2302 : tensor<384x128xf32>
+ %2304 = "mhlo.reshape"(%2303) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2305 = "mhlo.dot"(%2262, %1146) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2306 = "mhlo.broadcast_in_dim"(%1145) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2307 = mhlo.add %2305, %2306 : tensor<384x128xf32>
+ %2308 = "mhlo.reshape"(%2307) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2309 = "mhlo.broadcast_in_dim"(%1144) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2310 = mhlo.multiply %2308, %2309 : tensor<1x384x128xf32>
+ %2311 = "mhlo.broadcast_in_dim"(%1143) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2312 = mhlo.add %2310, %2311 : tensor<1x384x128xf32>
+ %2313 = mhlo.add %2304, %2312 : tensor<1x384x128xf32>
+ %2314 = "mhlo.broadcast_in_dim"(%1130) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2315 = mhlo.multiply %2313, %2314 : tensor<1x384x128xf32>
+ %2316 = "mhlo.broadcast_in_dim"(%1129) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2317 = mhlo.add %2315, %2316 : tensor<1x384x128xf32>
+ %2318 = "mhlo.reshape"(%2317) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2319 = "mhlo.dot"(%2318, %1148) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2320 = "mhlo.broadcast_in_dim"(%1147) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2321 = mhlo.add %2319, %2320 : tensor<384x512xf32>
+ %2322 = "mhlo.reshape"(%2321) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2323 = mhlo.maximum %2322, %1119 : tensor<1x384x512xf32>
+ %2324 = "mhlo.reshape"(%2323) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2325 = "mhlo.dot"(%2324, %1152) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2326 = "mhlo.broadcast_in_dim"(%1151) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2327 = mhlo.add %2325, %2326 : tensor<384x128xf32>
+ %2328 = "mhlo.reshape"(%2327) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2329 = mhlo.add %2328, %2317 : tensor<1x384x128xf32>
+ %2330 = "mhlo.broadcast_in_dim"(%1150) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2331 = mhlo.multiply %2329, %2330 : tensor<1x384x128xf32>
+ %2332 = "mhlo.broadcast_in_dim"(%1149) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2333 = mhlo.add %2331, %2332 : tensor<1x384x128xf32>
+ %2334 = "mhlo.reshape"(%2333) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2335 = "mhlo.dot"(%2334, %1154) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2336 = "mhlo.broadcast_in_dim"(%1153) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2337 = mhlo.add %2335, %2336 : tensor<384x512xf32>
+ %2338 = "mhlo.reshape"(%2337) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2339 = mhlo.maximum %2338, %1119 : tensor<1x384x512xf32>
+ %2340 = "mhlo.reshape"(%2339) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2341 = "mhlo.dot"(%2340, %1158) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2342 = "mhlo.broadcast_in_dim"(%1157) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2343 = mhlo.add %2341, %2342 : tensor<384x128xf32>
+ %2344 = "mhlo.reshape"(%2343) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2345 = mhlo.add %2344, %2333 : tensor<1x384x128xf32>
+ %2346 = "mhlo.broadcast_in_dim"(%1156) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2347 = mhlo.multiply %2345, %2346 : tensor<1x384x128xf32>
+ %2348 = "mhlo.broadcast_in_dim"(%1155) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2349 = mhlo.add %2347, %2348 : tensor<1x384x128xf32>
+ %2350 = "mhlo.reshape"(%2349) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2351 = "mhlo.dot"(%2350, %1160) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2352 = "mhlo.broadcast_in_dim"(%1159) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2353 = mhlo.add %2351, %2352 : tensor<384x512xf32>
+ %2354 = "mhlo.reshape"(%2353) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2355 = mhlo.maximum %2354, %1119 : tensor<1x384x512xf32>
+ %2356 = "mhlo.reshape"(%2355) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2357 = "mhlo.dot"(%2356, %1164) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2358 = "mhlo.broadcast_in_dim"(%1163) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2359 = mhlo.add %2357, %2358 : tensor<384x128xf32>
+ %2360 = "mhlo.reshape"(%2359) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2361 = mhlo.add %2360, %2349 : tensor<1x384x128xf32>
+ %2362 = "mhlo.broadcast_in_dim"(%1162) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2363 = mhlo.multiply %2361, %2362 : tensor<1x384x128xf32>
+ %2364 = "mhlo.broadcast_in_dim"(%1161) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2365 = mhlo.add %2363, %2364 : tensor<1x384x128xf32>
+ %2366 = "mhlo.reshape"(%2365) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2367 = "mhlo.dot"(%2366, %1166) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2368 = "mhlo.broadcast_in_dim"(%1165) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2369 = mhlo.add %2367, %2368 : tensor<384x512xf32>
+ %2370 = "mhlo.reshape"(%2369) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2371 = mhlo.maximum %2370, %1119 : tensor<1x384x512xf32>
+ %2372 = "mhlo.reshape"(%2371) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2373 = "mhlo.dot"(%2372, %1174) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2374 = "mhlo.broadcast_in_dim"(%1173) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2375 = mhlo.add %2373, %2374 : tensor<384x128xf32>
+ %2376 = "mhlo.reshape"(%2375) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2377 = mhlo.add %2376, %2365 : tensor<1x384x128xf32>
+ %2378 = "mhlo.broadcast_in_dim"(%1168) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2379 = mhlo.multiply %2377, %2378 : tensor<1x384x128xf32>
+ %2380 = "mhlo.broadcast_in_dim"(%1167) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2381 = mhlo.add %2379, %2380 : tensor<1x384x128xf32>
+ %2382 = "mhlo.reshape"(%2381) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2383 = "mhlo.dot"(%2382, %1172) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2384 = "mhlo.broadcast_in_dim"(%1171) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2385 = mhlo.add %2383, %2384 : tensor<384x512xf32>
+ %2386 = "mhlo.reshape"(%2385) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2387 = mhlo.add %2386, %2261 : tensor<1x384x512xf32>
+ %2388 = "mhlo.broadcast_in_dim"(%1170) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2389 = mhlo.multiply %2387, %2388 : tensor<1x384x512xf32>
+ %2390 = "mhlo.broadcast_in_dim"(%1169) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2391 = mhlo.add %2389, %2390 : tensor<1x384x512xf32>
+ %2392 = "mhlo.reshape"(%2391) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2393 = "mhlo.dot"(%2392, %1184) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2394 = "mhlo.broadcast_in_dim"(%1183) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2395 = mhlo.add %2393, %2394 : tensor<384x128xf32>
+ %2396 = "mhlo.reshape"(%2395) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2397 = "mhlo.transpose"(%2396) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2398 = "mhlo.dot"(%2392, %1188) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2399 = "mhlo.reshape"(%2398) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2400 = "mhlo.broadcast_in_dim"(%1187) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2401 = mhlo.add %2399, %2400 : tensor<1x384x128xf32>
+ %2402 = "mhlo.broadcast_in_dim"(%1186) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2403 = mhlo.multiply %2401, %2402 : tensor<1x384x128xf32>
+ %2404 = "mhlo.broadcast_in_dim"(%1185) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2405 = mhlo.add %2403, %2404 : tensor<1x384x128xf32>
+ %2406 = "mhlo.reshape"(%2405) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2407 = "mhlo.dot"(%2406, %1180) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2408 = "mhlo.broadcast_in_dim"(%1179) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2409 = mhlo.add %2407, %2408 : tensor<384x128xf32>
+ %2410 = "mhlo.reshape"(%2409) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2411 = "mhlo.transpose"(%2410) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2412 = "mhlo.dot"(%2406, %1182) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2413 = "mhlo.broadcast_in_dim"(%1181) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2414 = mhlo.add %2412, %2413 : tensor<384x128xf32>
+ %2415 = "mhlo.reshape"(%2414) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2416 = "mhlo.transpose"(%2415) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2417 = "mhlo.dot_general"(%2416, %2411) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2418 = mhlo.multiply %2417, %1114 : tensor<1x4x384x384xf32>
+ %2419 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2420 = mhlo.add %2418, %2419 : tensor<1x4x384x384xf32>
+ %2421 = "mhlo.reduce"(%2420, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2422 = "mhlo.broadcast_in_dim"(%2421) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2423 = mhlo.subtract %2420, %2422 : tensor<1x4x384x384xf32>
+ %2424 = "mhlo.exponential"(%2423) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2425 = "mhlo.reduce"(%2424, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2426 = "mhlo.broadcast_in_dim"(%2425) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2427 = mhlo.divide %2424, %2426 : tensor<1x4x384x384xf32>
+ %2428 = "mhlo.dot_general"(%2427, %2397) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2429 = "mhlo.transpose"(%2428) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2430 = "mhlo.reshape"(%2429) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2431 = "mhlo.dot"(%2430, %1178) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2432 = "mhlo.broadcast_in_dim"(%1177) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2433 = mhlo.add %2431, %2432 : tensor<384x128xf32>
+ %2434 = "mhlo.reshape"(%2433) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2435 = "mhlo.dot"(%2392, %1192) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2436 = "mhlo.broadcast_in_dim"(%1191) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2437 = mhlo.add %2435, %2436 : tensor<384x128xf32>
+ %2438 = "mhlo.reshape"(%2437) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2439 = "mhlo.broadcast_in_dim"(%1190) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2440 = mhlo.multiply %2438, %2439 : tensor<1x384x128xf32>
+ %2441 = "mhlo.broadcast_in_dim"(%1189) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2442 = mhlo.add %2440, %2441 : tensor<1x384x128xf32>
+ %2443 = mhlo.add %2434, %2442 : tensor<1x384x128xf32>
+ %2444 = "mhlo.broadcast_in_dim"(%1176) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2445 = mhlo.multiply %2443, %2444 : tensor<1x384x128xf32>
+ %2446 = "mhlo.broadcast_in_dim"(%1175) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2447 = mhlo.add %2445, %2446 : tensor<1x384x128xf32>
+ %2448 = "mhlo.reshape"(%2447) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2449 = "mhlo.dot"(%2448, %1194) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2450 = "mhlo.broadcast_in_dim"(%1193) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2451 = mhlo.add %2449, %2450 : tensor<384x512xf32>
+ %2452 = "mhlo.reshape"(%2451) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2453 = mhlo.maximum %2452, %1119 : tensor<1x384x512xf32>
+ %2454 = "mhlo.reshape"(%2453) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2455 = "mhlo.dot"(%2454, %1198) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2456 = "mhlo.broadcast_in_dim"(%1197) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2457 = mhlo.add %2455, %2456 : tensor<384x128xf32>
+ %2458 = "mhlo.reshape"(%2457) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2459 = mhlo.add %2458, %2447 : tensor<1x384x128xf32>
+ %2460 = "mhlo.broadcast_in_dim"(%1196) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2461 = mhlo.multiply %2459, %2460 : tensor<1x384x128xf32>
+ %2462 = "mhlo.broadcast_in_dim"(%1195) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2463 = mhlo.add %2461, %2462 : tensor<1x384x128xf32>
+ %2464 = "mhlo.reshape"(%2463) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2465 = "mhlo.dot"(%2464, %1200) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2466 = "mhlo.broadcast_in_dim"(%1199) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2467 = mhlo.add %2465, %2466 : tensor<384x512xf32>
+ %2468 = "mhlo.reshape"(%2467) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2469 = mhlo.maximum %2468, %1119 : tensor<1x384x512xf32>
+ %2470 = "mhlo.reshape"(%2469) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2471 = "mhlo.dot"(%2470, %1204) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2472 = "mhlo.broadcast_in_dim"(%1203) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2473 = mhlo.add %2471, %2472 : tensor<384x128xf32>
+ %2474 = "mhlo.reshape"(%2473) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2475 = mhlo.add %2474, %2463 : tensor<1x384x128xf32>
+ %2476 = "mhlo.broadcast_in_dim"(%1202) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2477 = mhlo.multiply %2475, %2476 : tensor<1x384x128xf32>
+ %2478 = "mhlo.broadcast_in_dim"(%1201) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2479 = mhlo.add %2477, %2478 : tensor<1x384x128xf32>
+ %2480 = "mhlo.reshape"(%2479) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2481 = "mhlo.dot"(%2480, %1206) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2482 = "mhlo.broadcast_in_dim"(%1205) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2483 = mhlo.add %2481, %2482 : tensor<384x512xf32>
+ %2484 = "mhlo.reshape"(%2483) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2485 = mhlo.maximum %2484, %1119 : tensor<1x384x512xf32>
+ %2486 = "mhlo.reshape"(%2485) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2487 = "mhlo.dot"(%2486, %1210) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2488 = "mhlo.broadcast_in_dim"(%1209) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2489 = mhlo.add %2487, %2488 : tensor<384x128xf32>
+ %2490 = "mhlo.reshape"(%2489) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2491 = mhlo.add %2490, %2479 : tensor<1x384x128xf32>
+ %2492 = "mhlo.broadcast_in_dim"(%1208) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2493 = mhlo.multiply %2491, %2492 : tensor<1x384x128xf32>
+ %2494 = "mhlo.broadcast_in_dim"(%1207) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2495 = mhlo.add %2493, %2494 : tensor<1x384x128xf32>
+ %2496 = "mhlo.reshape"(%2495) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2497 = "mhlo.dot"(%2496, %1212) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2498 = "mhlo.broadcast_in_dim"(%1211) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2499 = mhlo.add %2497, %2498 : tensor<384x512xf32>
+ %2500 = "mhlo.reshape"(%2499) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2501 = mhlo.maximum %2500, %1119 : tensor<1x384x512xf32>
+ %2502 = "mhlo.reshape"(%2501) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2503 = "mhlo.dot"(%2502, %1220) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2504 = "mhlo.broadcast_in_dim"(%1219) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2505 = mhlo.add %2503, %2504 : tensor<384x128xf32>
+ %2506 = "mhlo.reshape"(%2505) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2507 = mhlo.add %2506, %2495 : tensor<1x384x128xf32>
+ %2508 = "mhlo.broadcast_in_dim"(%1214) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2509 = mhlo.multiply %2507, %2508 : tensor<1x384x128xf32>
+ %2510 = "mhlo.broadcast_in_dim"(%1213) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2511 = mhlo.add %2509, %2510 : tensor<1x384x128xf32>
+ %2512 = "mhlo.reshape"(%2511) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2513 = "mhlo.dot"(%2512, %1218) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2514 = "mhlo.broadcast_in_dim"(%1217) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2515 = mhlo.add %2513, %2514 : tensor<384x512xf32>
+ %2516 = "mhlo.reshape"(%2515) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2517 = mhlo.add %2516, %2391 : tensor<1x384x512xf32>
+ %2518 = "mhlo.broadcast_in_dim"(%1216) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2519 = mhlo.multiply %2517, %2518 : tensor<1x384x512xf32>
+ %2520 = "mhlo.broadcast_in_dim"(%1215) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2521 = mhlo.add %2519, %2520 : tensor<1x384x512xf32>
+ %2522 = "mhlo.reshape"(%2521) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2523 = "mhlo.dot"(%2522, %1690) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2524 = "mhlo.broadcast_in_dim"(%1689) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2525 = mhlo.add %2523, %2524 : tensor<384x128xf32>
+ %2526 = "mhlo.reshape"(%2525) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2527 = "mhlo.transpose"(%2526) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2528 = "mhlo.dot"(%2522, %1694) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2529 = "mhlo.reshape"(%2528) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2530 = "mhlo.broadcast_in_dim"(%1693) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2531 = mhlo.add %2529, %2530 : tensor<1x384x128xf32>
+ %2532 = "mhlo.broadcast_in_dim"(%1692) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2533 = mhlo.multiply %2531, %2532 : tensor<1x384x128xf32>
+ %2534 = "mhlo.broadcast_in_dim"(%1691) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2535 = mhlo.add %2533, %2534 : tensor<1x384x128xf32>
+ %2536 = "mhlo.reshape"(%2535) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2537 = "mhlo.dot"(%2536, %1686) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2538 = "mhlo.broadcast_in_dim"(%1685) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2539 = mhlo.add %2537, %2538 : tensor<384x128xf32>
+ %2540 = "mhlo.reshape"(%2539) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2541 = "mhlo.transpose"(%2540) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2542 = "mhlo.dot"(%2536, %1688) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2543 = "mhlo.broadcast_in_dim"(%1687) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2544 = mhlo.add %2542, %2543 : tensor<384x128xf32>
+ %2545 = "mhlo.reshape"(%2544) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2546 = "mhlo.transpose"(%2545) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2547 = "mhlo.dot_general"(%2546, %2541) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2548 = mhlo.multiply %2547, %1114 : tensor<1x4x384x384xf32>
+ %2549 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2550 = mhlo.add %2548, %2549 : tensor<1x4x384x384xf32>
+ %2551 = "mhlo.reduce"(%2550, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2552 = "mhlo.broadcast_in_dim"(%2551) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2553 = mhlo.subtract %2550, %2552 : tensor<1x4x384x384xf32>
+ %2554 = "mhlo.exponential"(%2553) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2555 = "mhlo.reduce"(%2554, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2556 = "mhlo.broadcast_in_dim"(%2555) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2557 = mhlo.divide %2554, %2556 : tensor<1x4x384x384xf32>
+ %2558 = "mhlo.dot_general"(%2557, %2527) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2559 = "mhlo.transpose"(%2558) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2560 = "mhlo.reshape"(%2559) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2561 = "mhlo.dot"(%2560, %1684) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2562 = "mhlo.broadcast_in_dim"(%1683) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2563 = mhlo.add %2561, %2562 : tensor<384x128xf32>
+ %2564 = "mhlo.reshape"(%2563) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2565 = "mhlo.dot"(%2522, %1698) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2566 = "mhlo.broadcast_in_dim"(%1697) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2567 = mhlo.add %2565, %2566 : tensor<384x128xf32>
+ %2568 = "mhlo.reshape"(%2567) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2569 = "mhlo.broadcast_in_dim"(%1696) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2570 = mhlo.multiply %2568, %2569 : tensor<1x384x128xf32>
+ %2571 = "mhlo.broadcast_in_dim"(%1695) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2572 = mhlo.add %2570, %2571 : tensor<1x384x128xf32>
+ %2573 = mhlo.add %2564, %2572 : tensor<1x384x128xf32>
+ %2574 = "mhlo.broadcast_in_dim"(%1682) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2575 = mhlo.multiply %2573, %2574 : tensor<1x384x128xf32>
+ %2576 = "mhlo.broadcast_in_dim"(%1681) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2577 = mhlo.add %2575, %2576 : tensor<1x384x128xf32>
+ %2578 = "mhlo.reshape"(%2577) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2579 = "mhlo.dot"(%2578, %1700) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2580 = "mhlo.broadcast_in_dim"(%1699) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2581 = mhlo.add %2579, %2580 : tensor<384x512xf32>
+ %2582 = "mhlo.reshape"(%2581) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2583 = mhlo.maximum %2582, %1119 : tensor<1x384x512xf32>
+ %2584 = "mhlo.reshape"(%2583) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2585 = "mhlo.dot"(%2584, %1704) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2586 = "mhlo.broadcast_in_dim"(%1703) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2587 = mhlo.add %2585, %2586 : tensor<384x128xf32>
+ %2588 = "mhlo.reshape"(%2587) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2589 = mhlo.add %2588, %2577 : tensor<1x384x128xf32>
+ %2590 = "mhlo.broadcast_in_dim"(%1702) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2591 = mhlo.multiply %2589, %2590 : tensor<1x384x128xf32>
+ %2592 = "mhlo.broadcast_in_dim"(%1701) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2593 = mhlo.add %2591, %2592 : tensor<1x384x128xf32>
+ %2594 = "mhlo.reshape"(%2593) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2595 = "mhlo.dot"(%2594, %1706) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2596 = "mhlo.broadcast_in_dim"(%1705) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2597 = mhlo.add %2595, %2596 : tensor<384x512xf32>
+ %2598 = "mhlo.reshape"(%2597) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2599 = mhlo.maximum %2598, %1119 : tensor<1x384x512xf32>
+ %2600 = "mhlo.reshape"(%2599) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2601 = "mhlo.dot"(%2600, %1710) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2602 = "mhlo.broadcast_in_dim"(%1709) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2603 = mhlo.add %2601, %2602 : tensor<384x128xf32>
+ %2604 = "mhlo.reshape"(%2603) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2605 = mhlo.add %2604, %2593 : tensor<1x384x128xf32>
+ %2606 = "mhlo.broadcast_in_dim"(%1708) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2607 = mhlo.multiply %2605, %2606 : tensor<1x384x128xf32>
+ %2608 = "mhlo.broadcast_in_dim"(%1707) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2609 = mhlo.add %2607, %2608 : tensor<1x384x128xf32>
+ %2610 = "mhlo.reshape"(%2609) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2611 = "mhlo.dot"(%2610, %1712) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2612 = "mhlo.broadcast_in_dim"(%1711) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2613 = mhlo.add %2611, %2612 : tensor<384x512xf32>
+ %2614 = "mhlo.reshape"(%2613) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2615 = mhlo.maximum %2614, %1119 : tensor<1x384x512xf32>
+ %2616 = "mhlo.reshape"(%2615) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2617 = "mhlo.dot"(%2616, %1716) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2618 = "mhlo.broadcast_in_dim"(%1715) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2619 = mhlo.add %2617, %2618 : tensor<384x128xf32>
+ %2620 = "mhlo.reshape"(%2619) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2621 = mhlo.add %2620, %2609 : tensor<1x384x128xf32>
+ %2622 = "mhlo.broadcast_in_dim"(%1714) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2623 = mhlo.multiply %2621, %2622 : tensor<1x384x128xf32>
+ %2624 = "mhlo.broadcast_in_dim"(%1713) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2625 = mhlo.add %2623, %2624 : tensor<1x384x128xf32>
+ %2626 = "mhlo.reshape"(%2625) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2627 = "mhlo.dot"(%2626, %1718) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2628 = "mhlo.broadcast_in_dim"(%1717) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2629 = mhlo.add %2627, %2628 : tensor<384x512xf32>
+ %2630 = "mhlo.reshape"(%2629) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2631 = mhlo.maximum %2630, %1119 : tensor<1x384x512xf32>
+ %2632 = "mhlo.reshape"(%2631) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2633 = "mhlo.dot"(%2632, %1726) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2634 = "mhlo.broadcast_in_dim"(%1725) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2635 = mhlo.add %2633, %2634 : tensor<384x128xf32>
+ %2636 = "mhlo.reshape"(%2635) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2637 = mhlo.add %2636, %2625 : tensor<1x384x128xf32>
+ %2638 = "mhlo.broadcast_in_dim"(%1720) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2639 = mhlo.multiply %2637, %2638 : tensor<1x384x128xf32>
+ %2640 = "mhlo.broadcast_in_dim"(%1719) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2641 = mhlo.add %2639, %2640 : tensor<1x384x128xf32>
+ %2642 = "mhlo.reshape"(%2641) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2643 = "mhlo.dot"(%2642, %1724) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2644 = "mhlo.broadcast_in_dim"(%1723) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2645 = mhlo.add %2643, %2644 : tensor<384x512xf32>
+ %2646 = "mhlo.reshape"(%2645) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2647 = mhlo.add %2646, %2521 : tensor<1x384x512xf32>
+ %2648 = "mhlo.broadcast_in_dim"(%1722) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2649 = mhlo.multiply %2647, %2648 : tensor<1x384x512xf32>
+ %2650 = "mhlo.broadcast_in_dim"(%1721) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2651 = mhlo.add %2649, %2650 : tensor<1x384x512xf32>
+ %2652 = "mhlo.reshape"(%2651) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2653 = "mhlo.dot"(%2652, %1920) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2654 = "mhlo.broadcast_in_dim"(%1919) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2655 = mhlo.add %2653, %2654 : tensor<384x128xf32>
+ %2656 = "mhlo.reshape"(%2655) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2657 = "mhlo.transpose"(%2656) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2658 = "mhlo.dot"(%2652, %1924) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2659 = "mhlo.reshape"(%2658) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2660 = "mhlo.broadcast_in_dim"(%1923) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2661 = mhlo.add %2659, %2660 : tensor<1x384x128xf32>
+ %2662 = "mhlo.broadcast_in_dim"(%1922) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2663 = mhlo.multiply %2661, %2662 : tensor<1x384x128xf32>
+ %2664 = "mhlo.broadcast_in_dim"(%1921) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2665 = mhlo.add %2663, %2664 : tensor<1x384x128xf32>
+ %2666 = "mhlo.reshape"(%2665) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2667 = "mhlo.dot"(%2666, %1916) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2668 = "mhlo.broadcast_in_dim"(%1915) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2669 = mhlo.add %2667, %2668 : tensor<384x128xf32>
+ %2670 = "mhlo.reshape"(%2669) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2671 = "mhlo.transpose"(%2670) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2672 = "mhlo.dot"(%2666, %1918) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2673 = "mhlo.broadcast_in_dim"(%1917) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2674 = mhlo.add %2672, %2673 : tensor<384x128xf32>
+ %2675 = "mhlo.reshape"(%2674) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2676 = "mhlo.transpose"(%2675) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2677 = "mhlo.dot_general"(%2676, %2671) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2678 = mhlo.multiply %2677, %1114 : tensor<1x4x384x384xf32>
+ %2679 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2680 = mhlo.add %2678, %2679 : tensor<1x4x384x384xf32>
+ %2681 = "mhlo.reduce"(%2680, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2682 = "mhlo.broadcast_in_dim"(%2681) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2683 = mhlo.subtract %2680, %2682 : tensor<1x4x384x384xf32>
+ %2684 = "mhlo.exponential"(%2683) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2685 = "mhlo.reduce"(%2684, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2686 = "mhlo.broadcast_in_dim"(%2685) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2687 = mhlo.divide %2684, %2686 : tensor<1x4x384x384xf32>
+ %2688 = "mhlo.dot_general"(%2687, %2657) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2689 = "mhlo.transpose"(%2688) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2690 = "mhlo.reshape"(%2689) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2691 = "mhlo.dot"(%2690, %1914) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2692 = "mhlo.broadcast_in_dim"(%1913) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2693 = mhlo.add %2691, %2692 : tensor<384x128xf32>
+ %2694 = "mhlo.reshape"(%2693) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2695 = "mhlo.dot"(%2652, %1928) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2696 = "mhlo.broadcast_in_dim"(%1927) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2697 = mhlo.add %2695, %2696 : tensor<384x128xf32>
+ %2698 = "mhlo.reshape"(%2697) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2699 = "mhlo.broadcast_in_dim"(%1926) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2700 = mhlo.multiply %2698, %2699 : tensor<1x384x128xf32>
+ %2701 = "mhlo.broadcast_in_dim"(%1925) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2702 = mhlo.add %2700, %2701 : tensor<1x384x128xf32>
+ %2703 = mhlo.add %2694, %2702 : tensor<1x384x128xf32>
+ %2704 = "mhlo.broadcast_in_dim"(%1912) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2705 = mhlo.multiply %2703, %2704 : tensor<1x384x128xf32>
+ %2706 = "mhlo.broadcast_in_dim"(%1911) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2707 = mhlo.add %2705, %2706 : tensor<1x384x128xf32>
+ %2708 = "mhlo.reshape"(%2707) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2709 = "mhlo.dot"(%2708, %1930) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2710 = "mhlo.broadcast_in_dim"(%1929) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2711 = mhlo.add %2709, %2710 : tensor<384x512xf32>
+ %2712 = "mhlo.reshape"(%2711) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2713 = mhlo.maximum %2712, %1119 : tensor<1x384x512xf32>
+ %2714 = "mhlo.reshape"(%2713) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2715 = "mhlo.dot"(%2714, %1934) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2716 = "mhlo.broadcast_in_dim"(%1933) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2717 = mhlo.add %2715, %2716 : tensor<384x128xf32>
+ %2718 = "mhlo.reshape"(%2717) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2719 = mhlo.add %2718, %2707 : tensor<1x384x128xf32>
+ %2720 = "mhlo.broadcast_in_dim"(%1932) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2721 = mhlo.multiply %2719, %2720 : tensor<1x384x128xf32>
+ %2722 = "mhlo.broadcast_in_dim"(%1931) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2723 = mhlo.add %2721, %2722 : tensor<1x384x128xf32>
+ %2724 = "mhlo.reshape"(%2723) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2725 = "mhlo.dot"(%2724, %1936) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2726 = "mhlo.broadcast_in_dim"(%1935) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2727 = mhlo.add %2725, %2726 : tensor<384x512xf32>
+ %2728 = "mhlo.reshape"(%2727) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2729 = mhlo.maximum %2728, %1119 : tensor<1x384x512xf32>
+ %2730 = "mhlo.reshape"(%2729) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2731 = "mhlo.dot"(%2730, %1940) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2732 = "mhlo.broadcast_in_dim"(%1939) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2733 = mhlo.add %2731, %2732 : tensor<384x128xf32>
+ %2734 = "mhlo.reshape"(%2733) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2735 = mhlo.add %2734, %2723 : tensor<1x384x128xf32>
+ %2736 = "mhlo.broadcast_in_dim"(%1938) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2737 = mhlo.multiply %2735, %2736 : tensor<1x384x128xf32>
+ %2738 = "mhlo.broadcast_in_dim"(%1937) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2739 = mhlo.add %2737, %2738 : tensor<1x384x128xf32>
+ %2740 = "mhlo.reshape"(%2739) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2741 = "mhlo.dot"(%2740, %1942) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2742 = "mhlo.broadcast_in_dim"(%1941) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2743 = mhlo.add %2741, %2742 : tensor<384x512xf32>
+ %2744 = "mhlo.reshape"(%2743) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2745 = mhlo.maximum %2744, %1119 : tensor<1x384x512xf32>
+ %2746 = "mhlo.reshape"(%2745) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2747 = "mhlo.dot"(%2746, %1946) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2748 = "mhlo.broadcast_in_dim"(%1945) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2749 = mhlo.add %2747, %2748 : tensor<384x128xf32>
+ %2750 = "mhlo.reshape"(%2749) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2751 = mhlo.add %2750, %2739 : tensor<1x384x128xf32>
+ %2752 = "mhlo.broadcast_in_dim"(%1944) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2753 = mhlo.multiply %2751, %2752 : tensor<1x384x128xf32>
+ %2754 = "mhlo.broadcast_in_dim"(%1943) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2755 = mhlo.add %2753, %2754 : tensor<1x384x128xf32>
+ %2756 = "mhlo.reshape"(%2755) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2757 = "mhlo.dot"(%2756, %1948) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2758 = "mhlo.broadcast_in_dim"(%1947) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2759 = mhlo.add %2757, %2758 : tensor<384x512xf32>
+ %2760 = "mhlo.reshape"(%2759) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2761 = mhlo.maximum %2760, %1119 : tensor<1x384x512xf32>
+ %2762 = "mhlo.reshape"(%2761) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2763 = "mhlo.dot"(%2762, %1956) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2764 = "mhlo.broadcast_in_dim"(%1955) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2765 = mhlo.add %2763, %2764 : tensor<384x128xf32>
+ %2766 = "mhlo.reshape"(%2765) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2767 = mhlo.add %2766, %2755 : tensor<1x384x128xf32>
+ %2768 = "mhlo.broadcast_in_dim"(%1950) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2769 = mhlo.multiply %2767, %2768 : tensor<1x384x128xf32>
+ %2770 = "mhlo.broadcast_in_dim"(%1949) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2771 = mhlo.add %2769, %2770 : tensor<1x384x128xf32>
+ %2772 = "mhlo.reshape"(%2771) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2773 = "mhlo.dot"(%2772, %1954) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2774 = "mhlo.broadcast_in_dim"(%1953) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2775 = mhlo.add %2773, %2774 : tensor<384x512xf32>
+ %2776 = "mhlo.reshape"(%2775) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2777 = mhlo.add %2776, %2651 : tensor<1x384x512xf32>
+ %2778 = "mhlo.broadcast_in_dim"(%1952) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2779 = mhlo.multiply %2777, %2778 : tensor<1x384x512xf32>
+ %2780 = "mhlo.broadcast_in_dim"(%1951) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2781 = mhlo.add %2779, %2780 : tensor<1x384x512xf32>
+ %2782 = "mhlo.reshape"(%2781) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2783 = "mhlo.dot"(%2782, %1966) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2784 = "mhlo.broadcast_in_dim"(%1965) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2785 = mhlo.add %2783, %2784 : tensor<384x128xf32>
+ %2786 = "mhlo.reshape"(%2785) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2787 = "mhlo.transpose"(%2786) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2788 = "mhlo.dot"(%2782, %1970) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2789 = "mhlo.reshape"(%2788) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2790 = "mhlo.broadcast_in_dim"(%1969) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2791 = mhlo.add %2789, %2790 : tensor<1x384x128xf32>
+ %2792 = "mhlo.broadcast_in_dim"(%1968) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2793 = mhlo.multiply %2791, %2792 : tensor<1x384x128xf32>
+ %2794 = "mhlo.broadcast_in_dim"(%1967) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2795 = mhlo.add %2793, %2794 : tensor<1x384x128xf32>
+ %2796 = "mhlo.reshape"(%2795) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2797 = "mhlo.dot"(%2796, %1962) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2798 = "mhlo.broadcast_in_dim"(%1961) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2799 = mhlo.add %2797, %2798 : tensor<384x128xf32>
+ %2800 = "mhlo.reshape"(%2799) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2801 = "mhlo.transpose"(%2800) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2802 = "mhlo.dot"(%2796, %1964) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2803 = "mhlo.broadcast_in_dim"(%1963) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2804 = mhlo.add %2802, %2803 : tensor<384x128xf32>
+ %2805 = "mhlo.reshape"(%2804) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2806 = "mhlo.transpose"(%2805) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2807 = "mhlo.dot_general"(%2806, %2801) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2808 = mhlo.multiply %2807, %1114 : tensor<1x4x384x384xf32>
+ %2809 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2810 = mhlo.add %2808, %2809 : tensor<1x4x384x384xf32>
+ %2811 = "mhlo.reduce"(%2810, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2812 = "mhlo.broadcast_in_dim"(%2811) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2813 = mhlo.subtract %2810, %2812 : tensor<1x4x384x384xf32>
+ %2814 = "mhlo.exponential"(%2813) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2815 = "mhlo.reduce"(%2814, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2816 = "mhlo.broadcast_in_dim"(%2815) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2817 = mhlo.divide %2814, %2816 : tensor<1x4x384x384xf32>
+ %2818 = "mhlo.dot_general"(%2817, %2787) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2819 = "mhlo.transpose"(%2818) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2820 = "mhlo.reshape"(%2819) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2821 = "mhlo.dot"(%2820, %1960) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2822 = "mhlo.broadcast_in_dim"(%1959) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2823 = mhlo.add %2821, %2822 : tensor<384x128xf32>
+ %2824 = "mhlo.reshape"(%2823) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2825 = "mhlo.dot"(%2782, %1974) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2826 = "mhlo.broadcast_in_dim"(%1973) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2827 = mhlo.add %2825, %2826 : tensor<384x128xf32>
+ %2828 = "mhlo.reshape"(%2827) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2829 = "mhlo.broadcast_in_dim"(%1972) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2830 = mhlo.multiply %2828, %2829 : tensor<1x384x128xf32>
+ %2831 = "mhlo.broadcast_in_dim"(%1971) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2832 = mhlo.add %2830, %2831 : tensor<1x384x128xf32>
+ %2833 = mhlo.add %2824, %2832 : tensor<1x384x128xf32>
+ %2834 = "mhlo.broadcast_in_dim"(%1958) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2835 = mhlo.multiply %2833, %2834 : tensor<1x384x128xf32>
+ %2836 = "mhlo.broadcast_in_dim"(%1957) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2837 = mhlo.add %2835, %2836 : tensor<1x384x128xf32>
+ %2838 = "mhlo.reshape"(%2837) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2839 = "mhlo.dot"(%2838, %1976) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2840 = "mhlo.broadcast_in_dim"(%1975) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2841 = mhlo.add %2839, %2840 : tensor<384x512xf32>
+ %2842 = "mhlo.reshape"(%2841) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2843 = mhlo.maximum %2842, %1119 : tensor<1x384x512xf32>
+ %2844 = "mhlo.reshape"(%2843) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2845 = "mhlo.dot"(%2844, %1980) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2846 = "mhlo.broadcast_in_dim"(%1979) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2847 = mhlo.add %2845, %2846 : tensor<384x128xf32>
+ %2848 = "mhlo.reshape"(%2847) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2849 = mhlo.add %2848, %2837 : tensor<1x384x128xf32>
+ %2850 = "mhlo.broadcast_in_dim"(%1978) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2851 = mhlo.multiply %2849, %2850 : tensor<1x384x128xf32>
+ %2852 = "mhlo.broadcast_in_dim"(%1977) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2853 = mhlo.add %2851, %2852 : tensor<1x384x128xf32>
+ %2854 = "mhlo.reshape"(%2853) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2855 = "mhlo.dot"(%2854, %1982) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2856 = "mhlo.broadcast_in_dim"(%1981) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2857 = mhlo.add %2855, %2856 : tensor<384x512xf32>
+ %2858 = "mhlo.reshape"(%2857) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2859 = mhlo.maximum %2858, %1119 : tensor<1x384x512xf32>
+ %2860 = "mhlo.reshape"(%2859) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2861 = "mhlo.dot"(%2860, %1986) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2862 = "mhlo.broadcast_in_dim"(%1985) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2863 = mhlo.add %2861, %2862 : tensor<384x128xf32>
+ %2864 = "mhlo.reshape"(%2863) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2865 = mhlo.add %2864, %2853 : tensor<1x384x128xf32>
+ %2866 = "mhlo.broadcast_in_dim"(%1984) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2867 = mhlo.multiply %2865, %2866 : tensor<1x384x128xf32>
+ %2868 = "mhlo.broadcast_in_dim"(%1983) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2869 = mhlo.add %2867, %2868 : tensor<1x384x128xf32>
+ %2870 = "mhlo.reshape"(%2869) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2871 = "mhlo.dot"(%2870, %1988) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2872 = "mhlo.broadcast_in_dim"(%1987) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2873 = mhlo.add %2871, %2872 : tensor<384x512xf32>
+ %2874 = "mhlo.reshape"(%2873) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2875 = mhlo.maximum %2874, %1119 : tensor<1x384x512xf32>
+ %2876 = "mhlo.reshape"(%2875) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2877 = "mhlo.dot"(%2876, %1992) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2878 = "mhlo.broadcast_in_dim"(%1991) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2879 = mhlo.add %2877, %2878 : tensor<384x128xf32>
+ %2880 = "mhlo.reshape"(%2879) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2881 = mhlo.add %2880, %2869 : tensor<1x384x128xf32>
+ %2882 = "mhlo.broadcast_in_dim"(%1990) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2883 = mhlo.multiply %2881, %2882 : tensor<1x384x128xf32>
+ %2884 = "mhlo.broadcast_in_dim"(%1989) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2885 = mhlo.add %2883, %2884 : tensor<1x384x128xf32>
+ %2886 = "mhlo.reshape"(%2885) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2887 = "mhlo.dot"(%2886, %1994) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2888 = "mhlo.broadcast_in_dim"(%1993) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2889 = mhlo.add %2887, %2888 : tensor<384x512xf32>
+ %2890 = "mhlo.reshape"(%2889) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2891 = mhlo.maximum %2890, %1119 : tensor<1x384x512xf32>
+ %2892 = "mhlo.reshape"(%2891) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2893 = "mhlo.dot"(%2892, %2002) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2894 = "mhlo.broadcast_in_dim"(%2001) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2895 = mhlo.add %2893, %2894 : tensor<384x128xf32>
+ %2896 = "mhlo.reshape"(%2895) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2897 = mhlo.add %2896, %2885 : tensor<1x384x128xf32>
+ %2898 = "mhlo.broadcast_in_dim"(%1996) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2899 = mhlo.multiply %2897, %2898 : tensor<1x384x128xf32>
+ %2900 = "mhlo.broadcast_in_dim"(%1995) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2901 = mhlo.add %2899, %2900 : tensor<1x384x128xf32>
+ %2902 = "mhlo.reshape"(%2901) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2903 = "mhlo.dot"(%2902, %2000) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2904 = "mhlo.broadcast_in_dim"(%1999) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2905 = mhlo.add %2903, %2904 : tensor<384x512xf32>
+ %2906 = "mhlo.reshape"(%2905) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2907 = mhlo.add %2906, %2781 : tensor<1x384x512xf32>
+ %2908 = "mhlo.broadcast_in_dim"(%1998) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2909 = mhlo.multiply %2907, %2908 : tensor<1x384x512xf32>
+ %2910 = "mhlo.broadcast_in_dim"(%1997) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %2911 = mhlo.add %2909, %2910 : tensor<1x384x512xf32>
+ %2912 = "mhlo.reshape"(%2911) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2913 = "mhlo.dot"(%2912, %2012) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2914 = "mhlo.broadcast_in_dim"(%2011) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2915 = mhlo.add %2913, %2914 : tensor<384x128xf32>
+ %2916 = "mhlo.reshape"(%2915) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2917 = "mhlo.transpose"(%2916) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2918 = "mhlo.dot"(%2912, %2016) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2919 = "mhlo.reshape"(%2918) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2920 = "mhlo.broadcast_in_dim"(%2015) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2921 = mhlo.add %2919, %2920 : tensor<1x384x128xf32>
+ %2922 = "mhlo.broadcast_in_dim"(%2014) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2923 = mhlo.multiply %2921, %2922 : tensor<1x384x128xf32>
+ %2924 = "mhlo.broadcast_in_dim"(%2013) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2925 = mhlo.add %2923, %2924 : tensor<1x384x128xf32>
+ %2926 = "mhlo.reshape"(%2925) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2927 = "mhlo.dot"(%2926, %2008) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2928 = "mhlo.broadcast_in_dim"(%2007) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2929 = mhlo.add %2927, %2928 : tensor<384x128xf32>
+ %2930 = "mhlo.reshape"(%2929) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2931 = "mhlo.transpose"(%2930) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2932 = "mhlo.dot"(%2926, %2010) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2933 = "mhlo.broadcast_in_dim"(%2009) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2934 = mhlo.add %2932, %2933 : tensor<384x128xf32>
+ %2935 = "mhlo.reshape"(%2934) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %2936 = "mhlo.transpose"(%2935) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %2937 = "mhlo.dot_general"(%2936, %2931) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %2938 = mhlo.multiply %2937, %1114 : tensor<1x4x384x384xf32>
+ %2939 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2940 = mhlo.add %2938, %2939 : tensor<1x4x384x384xf32>
+ %2941 = "mhlo.reduce"(%2940, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2942 = "mhlo.broadcast_in_dim"(%2941) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2943 = mhlo.subtract %2940, %2942 : tensor<1x4x384x384xf32>
+ %2944 = "mhlo.exponential"(%2943) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %2945 = "mhlo.reduce"(%2944, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %2946 = "mhlo.broadcast_in_dim"(%2945) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %2947 = mhlo.divide %2944, %2946 : tensor<1x4x384x384xf32>
+ %2948 = "mhlo.dot_general"(%2947, %2917) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %2949 = "mhlo.transpose"(%2948) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %2950 = "mhlo.reshape"(%2949) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %2951 = "mhlo.dot"(%2950, %2006) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %2952 = "mhlo.broadcast_in_dim"(%2005) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2953 = mhlo.add %2951, %2952 : tensor<384x128xf32>
+ %2954 = "mhlo.reshape"(%2953) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2955 = "mhlo.dot"(%2912, %2020) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2956 = "mhlo.broadcast_in_dim"(%2019) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2957 = mhlo.add %2955, %2956 : tensor<384x128xf32>
+ %2958 = "mhlo.reshape"(%2957) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2959 = "mhlo.broadcast_in_dim"(%2018) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2960 = mhlo.multiply %2958, %2959 : tensor<1x384x128xf32>
+ %2961 = "mhlo.broadcast_in_dim"(%2017) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2962 = mhlo.add %2960, %2961 : tensor<1x384x128xf32>
+ %2963 = mhlo.add %2954, %2962 : tensor<1x384x128xf32>
+ %2964 = "mhlo.broadcast_in_dim"(%2004) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2965 = mhlo.multiply %2963, %2964 : tensor<1x384x128xf32>
+ %2966 = "mhlo.broadcast_in_dim"(%2003) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2967 = mhlo.add %2965, %2966 : tensor<1x384x128xf32>
+ %2968 = "mhlo.reshape"(%2967) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2969 = "mhlo.dot"(%2968, %2022) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2970 = "mhlo.broadcast_in_dim"(%2021) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2971 = mhlo.add %2969, %2970 : tensor<384x512xf32>
+ %2972 = "mhlo.reshape"(%2971) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2973 = mhlo.maximum %2972, %1119 : tensor<1x384x512xf32>
+ %2974 = "mhlo.reshape"(%2973) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2975 = "mhlo.dot"(%2974, %2026) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2976 = "mhlo.broadcast_in_dim"(%2025) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2977 = mhlo.add %2975, %2976 : tensor<384x128xf32>
+ %2978 = "mhlo.reshape"(%2977) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2979 = mhlo.add %2978, %2967 : tensor<1x384x128xf32>
+ %2980 = "mhlo.broadcast_in_dim"(%2024) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2981 = mhlo.multiply %2979, %2980 : tensor<1x384x128xf32>
+ %2982 = "mhlo.broadcast_in_dim"(%2023) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2983 = mhlo.add %2981, %2982 : tensor<1x384x128xf32>
+ %2984 = "mhlo.reshape"(%2983) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %2985 = "mhlo.dot"(%2984, %2028) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %2986 = "mhlo.broadcast_in_dim"(%2027) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %2987 = mhlo.add %2985, %2986 : tensor<384x512xf32>
+ %2988 = "mhlo.reshape"(%2987) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %2989 = mhlo.maximum %2988, %1119 : tensor<1x384x512xf32>
+ %2990 = "mhlo.reshape"(%2989) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %2991 = "mhlo.dot"(%2990, %2032) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %2992 = "mhlo.broadcast_in_dim"(%2031) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %2993 = mhlo.add %2991, %2992 : tensor<384x128xf32>
+ %2994 = "mhlo.reshape"(%2993) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %2995 = mhlo.add %2994, %2983 : tensor<1x384x128xf32>
+ %2996 = "mhlo.broadcast_in_dim"(%2030) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2997 = mhlo.multiply %2995, %2996 : tensor<1x384x128xf32>
+ %2998 = "mhlo.broadcast_in_dim"(%2029) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %2999 = mhlo.add %2997, %2998 : tensor<1x384x128xf32>
+ %3000 = "mhlo.reshape"(%2999) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3001 = "mhlo.dot"(%3000, %2034) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3002 = "mhlo.broadcast_in_dim"(%2033) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3003 = mhlo.add %3001, %3002 : tensor<384x512xf32>
+ %3004 = "mhlo.reshape"(%3003) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3005 = mhlo.maximum %3004, %1119 : tensor<1x384x512xf32>
+ %3006 = "mhlo.reshape"(%3005) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3007 = "mhlo.dot"(%3006, %2038) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3008 = "mhlo.broadcast_in_dim"(%2037) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3009 = mhlo.add %3007, %3008 : tensor<384x128xf32>
+ %3010 = "mhlo.reshape"(%3009) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3011 = mhlo.add %3010, %2999 : tensor<1x384x128xf32>
+ %3012 = "mhlo.broadcast_in_dim"(%2036) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3013 = mhlo.multiply %3011, %3012 : tensor<1x384x128xf32>
+ %3014 = "mhlo.broadcast_in_dim"(%2035) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3015 = mhlo.add %3013, %3014 : tensor<1x384x128xf32>
+ %3016 = "mhlo.reshape"(%3015) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3017 = "mhlo.dot"(%3016, %2040) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3018 = "mhlo.broadcast_in_dim"(%2039) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3019 = mhlo.add %3017, %3018 : tensor<384x512xf32>
+ %3020 = "mhlo.reshape"(%3019) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3021 = mhlo.maximum %3020, %1119 : tensor<1x384x512xf32>
+ %3022 = "mhlo.reshape"(%3021) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3023 = "mhlo.dot"(%3022, %2048) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3024 = "mhlo.broadcast_in_dim"(%2047) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3025 = mhlo.add %3023, %3024 : tensor<384x128xf32>
+ %3026 = "mhlo.reshape"(%3025) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3027 = mhlo.add %3026, %3015 : tensor<1x384x128xf32>
+ %3028 = "mhlo.broadcast_in_dim"(%2042) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3029 = mhlo.multiply %3027, %3028 : tensor<1x384x128xf32>
+ %3030 = "mhlo.broadcast_in_dim"(%2041) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3031 = mhlo.add %3029, %3030 : tensor<1x384x128xf32>
+ %3032 = "mhlo.reshape"(%3031) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3033 = "mhlo.dot"(%3032, %2046) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3034 = "mhlo.broadcast_in_dim"(%2045) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3035 = mhlo.add %3033, %3034 : tensor<384x512xf32>
+ %3036 = "mhlo.reshape"(%3035) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3037 = mhlo.add %3036, %2911 : tensor<1x384x512xf32>
+ %3038 = "mhlo.broadcast_in_dim"(%2044) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3039 = mhlo.multiply %3037, %3038 : tensor<1x384x512xf32>
+ %3040 = "mhlo.broadcast_in_dim"(%2043) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3041 = mhlo.add %3039, %3040 : tensor<1x384x512xf32>
+ %3042 = "mhlo.reshape"(%3041) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3043 = "mhlo.dot"(%3042, %2058) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3044 = "mhlo.broadcast_in_dim"(%2057) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3045 = mhlo.add %3043, %3044 : tensor<384x128xf32>
+ %3046 = "mhlo.reshape"(%3045) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3047 = "mhlo.transpose"(%3046) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3048 = "mhlo.dot"(%3042, %2062) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3049 = "mhlo.reshape"(%3048) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3050 = "mhlo.broadcast_in_dim"(%2061) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3051 = mhlo.add %3049, %3050 : tensor<1x384x128xf32>
+ %3052 = "mhlo.broadcast_in_dim"(%2060) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3053 = mhlo.multiply %3051, %3052 : tensor<1x384x128xf32>
+ %3054 = "mhlo.broadcast_in_dim"(%2059) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3055 = mhlo.add %3053, %3054 : tensor<1x384x128xf32>
+ %3056 = "mhlo.reshape"(%3055) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3057 = "mhlo.dot"(%3056, %2054) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3058 = "mhlo.broadcast_in_dim"(%2053) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3059 = mhlo.add %3057, %3058 : tensor<384x128xf32>
+ %3060 = "mhlo.reshape"(%3059) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3061 = "mhlo.transpose"(%3060) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3062 = "mhlo.dot"(%3056, %2056) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3063 = "mhlo.broadcast_in_dim"(%2055) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3064 = mhlo.add %3062, %3063 : tensor<384x128xf32>
+ %3065 = "mhlo.reshape"(%3064) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3066 = "mhlo.transpose"(%3065) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3067 = "mhlo.dot_general"(%3066, %3061) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3068 = mhlo.multiply %3067, %1114 : tensor<1x4x384x384xf32>
+ %3069 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3070 = mhlo.add %3068, %3069 : tensor<1x4x384x384xf32>
+ %3071 = "mhlo.reduce"(%3070, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3072 = "mhlo.broadcast_in_dim"(%3071) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3073 = mhlo.subtract %3070, %3072 : tensor<1x4x384x384xf32>
+ %3074 = "mhlo.exponential"(%3073) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3075 = "mhlo.reduce"(%3074, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3076 = "mhlo.broadcast_in_dim"(%3075) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3077 = mhlo.divide %3074, %3076 : tensor<1x4x384x384xf32>
+ %3078 = "mhlo.dot_general"(%3077, %3047) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3079 = "mhlo.transpose"(%3078) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3080 = "mhlo.reshape"(%3079) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3081 = "mhlo.dot"(%3080, %2052) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3082 = "mhlo.broadcast_in_dim"(%2051) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3083 = mhlo.add %3081, %3082 : tensor<384x128xf32>
+ %3084 = "mhlo.reshape"(%3083) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3085 = "mhlo.dot"(%3042, %2066) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3086 = "mhlo.broadcast_in_dim"(%2065) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3087 = mhlo.add %3085, %3086 : tensor<384x128xf32>
+ %3088 = "mhlo.reshape"(%3087) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3089 = "mhlo.broadcast_in_dim"(%2064) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3090 = mhlo.multiply %3088, %3089 : tensor<1x384x128xf32>
+ %3091 = "mhlo.broadcast_in_dim"(%2063) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3092 = mhlo.add %3090, %3091 : tensor<1x384x128xf32>
+ %3093 = mhlo.add %3084, %3092 : tensor<1x384x128xf32>
+ %3094 = "mhlo.broadcast_in_dim"(%2050) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3095 = mhlo.multiply %3093, %3094 : tensor<1x384x128xf32>
+ %3096 = "mhlo.broadcast_in_dim"(%2049) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3097 = mhlo.add %3095, %3096 : tensor<1x384x128xf32>
+ %3098 = "mhlo.reshape"(%3097) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3099 = "mhlo.dot"(%3098, %2068) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3100 = "mhlo.broadcast_in_dim"(%2067) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3101 = mhlo.add %3099, %3100 : tensor<384x512xf32>
+ %3102 = "mhlo.reshape"(%3101) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3103 = mhlo.maximum %3102, %1119 : tensor<1x384x512xf32>
+ %3104 = "mhlo.reshape"(%3103) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3105 = "mhlo.dot"(%3104, %2072) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3106 = "mhlo.broadcast_in_dim"(%2071) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3107 = mhlo.add %3105, %3106 : tensor<384x128xf32>
+ %3108 = "mhlo.reshape"(%3107) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3109 = mhlo.add %3108, %3097 : tensor<1x384x128xf32>
+ %3110 = "mhlo.broadcast_in_dim"(%2070) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3111 = mhlo.multiply %3109, %3110 : tensor<1x384x128xf32>
+ %3112 = "mhlo.broadcast_in_dim"(%2069) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3113 = mhlo.add %3111, %3112 : tensor<1x384x128xf32>
+ %3114 = "mhlo.reshape"(%3113) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3115 = "mhlo.dot"(%3114, %2074) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3116 = "mhlo.broadcast_in_dim"(%2073) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3117 = mhlo.add %3115, %3116 : tensor<384x512xf32>
+ %3118 = "mhlo.reshape"(%3117) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3119 = mhlo.maximum %3118, %1119 : tensor<1x384x512xf32>
+ %3120 = "mhlo.reshape"(%3119) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3121 = "mhlo.dot"(%3120, %2078) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3122 = "mhlo.broadcast_in_dim"(%2077) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3123 = mhlo.add %3121, %3122 : tensor<384x128xf32>
+ %3124 = "mhlo.reshape"(%3123) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3125 = mhlo.add %3124, %3113 : tensor<1x384x128xf32>
+ %3126 = "mhlo.broadcast_in_dim"(%2076) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3127 = mhlo.multiply %3125, %3126 : tensor<1x384x128xf32>
+ %3128 = "mhlo.broadcast_in_dim"(%2075) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3129 = mhlo.add %3127, %3128 : tensor<1x384x128xf32>
+ %3130 = "mhlo.reshape"(%3129) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3131 = "mhlo.dot"(%3130, %2080) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3132 = "mhlo.broadcast_in_dim"(%2079) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3133 = mhlo.add %3131, %3132 : tensor<384x512xf32>
+ %3134 = "mhlo.reshape"(%3133) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3135 = mhlo.maximum %3134, %1119 : tensor<1x384x512xf32>
+ %3136 = "mhlo.reshape"(%3135) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3137 = "mhlo.dot"(%3136, %2084) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3138 = "mhlo.broadcast_in_dim"(%2083) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3139 = mhlo.add %3137, %3138 : tensor<384x128xf32>
+ %3140 = "mhlo.reshape"(%3139) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3141 = mhlo.add %3140, %3129 : tensor<1x384x128xf32>
+ %3142 = "mhlo.broadcast_in_dim"(%2082) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3143 = mhlo.multiply %3141, %3142 : tensor<1x384x128xf32>
+ %3144 = "mhlo.broadcast_in_dim"(%2081) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3145 = mhlo.add %3143, %3144 : tensor<1x384x128xf32>
+ %3146 = "mhlo.reshape"(%3145) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3147 = "mhlo.dot"(%3146, %2086) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3148 = "mhlo.broadcast_in_dim"(%2085) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3149 = mhlo.add %3147, %3148 : tensor<384x512xf32>
+ %3150 = "mhlo.reshape"(%3149) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3151 = mhlo.maximum %3150, %1119 : tensor<1x384x512xf32>
+ %3152 = "mhlo.reshape"(%3151) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3153 = "mhlo.dot"(%3152, %2094) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3154 = "mhlo.broadcast_in_dim"(%2093) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3155 = mhlo.add %3153, %3154 : tensor<384x128xf32>
+ %3156 = "mhlo.reshape"(%3155) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3157 = mhlo.add %3156, %3145 : tensor<1x384x128xf32>
+ %3158 = "mhlo.broadcast_in_dim"(%2088) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3159 = mhlo.multiply %3157, %3158 : tensor<1x384x128xf32>
+ %3160 = "mhlo.broadcast_in_dim"(%2087) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3161 = mhlo.add %3159, %3160 : tensor<1x384x128xf32>
+ %3162 = "mhlo.reshape"(%3161) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3163 = "mhlo.dot"(%3162, %2092) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3164 = "mhlo.broadcast_in_dim"(%2091) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3165 = mhlo.add %3163, %3164 : tensor<384x512xf32>
+ %3166 = "mhlo.reshape"(%3165) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3167 = mhlo.add %3166, %3041 : tensor<1x384x512xf32>
+ %3168 = "mhlo.broadcast_in_dim"(%2090) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3169 = mhlo.multiply %3167, %3168 : tensor<1x384x512xf32>
+ %3170 = "mhlo.broadcast_in_dim"(%2089) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3171 = mhlo.add %3169, %3170 : tensor<1x384x512xf32>
+ %3172 = "mhlo.reshape"(%3171) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3173 = "mhlo.dot"(%3172, %2104) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3174 = "mhlo.broadcast_in_dim"(%2103) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3175 = mhlo.add %3173, %3174 : tensor<384x128xf32>
+ %3176 = "mhlo.reshape"(%3175) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3177 = "mhlo.transpose"(%3176) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3178 = "mhlo.dot"(%3172, %2108) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3179 = "mhlo.reshape"(%3178) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3180 = "mhlo.broadcast_in_dim"(%2107) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3181 = mhlo.add %3179, %3180 : tensor<1x384x128xf32>
+ %3182 = "mhlo.broadcast_in_dim"(%2106) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3183 = mhlo.multiply %3181, %3182 : tensor<1x384x128xf32>
+ %3184 = "mhlo.broadcast_in_dim"(%2105) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3185 = mhlo.add %3183, %3184 : tensor<1x384x128xf32>
+ %3186 = "mhlo.reshape"(%3185) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3187 = "mhlo.dot"(%3186, %2100) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3188 = "mhlo.broadcast_in_dim"(%2099) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3189 = mhlo.add %3187, %3188 : tensor<384x128xf32>
+ %3190 = "mhlo.reshape"(%3189) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3191 = "mhlo.transpose"(%3190) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3192 = "mhlo.dot"(%3186, %2102) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3193 = "mhlo.broadcast_in_dim"(%2101) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3194 = mhlo.add %3192, %3193 : tensor<384x128xf32>
+ %3195 = "mhlo.reshape"(%3194) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3196 = "mhlo.transpose"(%3195) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3197 = "mhlo.dot_general"(%3196, %3191) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3198 = mhlo.multiply %3197, %1114 : tensor<1x4x384x384xf32>
+ %3199 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3200 = mhlo.add %3198, %3199 : tensor<1x4x384x384xf32>
+ %3201 = "mhlo.reduce"(%3200, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3202 = "mhlo.broadcast_in_dim"(%3201) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3203 = mhlo.subtract %3200, %3202 : tensor<1x4x384x384xf32>
+ %3204 = "mhlo.exponential"(%3203) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3205 = "mhlo.reduce"(%3204, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3206 = "mhlo.broadcast_in_dim"(%3205) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3207 = mhlo.divide %3204, %3206 : tensor<1x4x384x384xf32>
+ %3208 = "mhlo.dot_general"(%3207, %3177) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3209 = "mhlo.transpose"(%3208) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3210 = "mhlo.reshape"(%3209) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3211 = "mhlo.dot"(%3210, %2098) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3212 = "mhlo.broadcast_in_dim"(%2097) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3213 = mhlo.add %3211, %3212 : tensor<384x128xf32>
+ %3214 = "mhlo.reshape"(%3213) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3215 = "mhlo.dot"(%3172, %2112) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3216 = "mhlo.broadcast_in_dim"(%2111) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3217 = mhlo.add %3215, %3216 : tensor<384x128xf32>
+ %3218 = "mhlo.reshape"(%3217) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3219 = "mhlo.broadcast_in_dim"(%2110) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3220 = mhlo.multiply %3218, %3219 : tensor<1x384x128xf32>
+ %3221 = "mhlo.broadcast_in_dim"(%2109) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3222 = mhlo.add %3220, %3221 : tensor<1x384x128xf32>
+ %3223 = mhlo.add %3214, %3222 : tensor<1x384x128xf32>
+ %3224 = "mhlo.broadcast_in_dim"(%2096) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3225 = mhlo.multiply %3223, %3224 : tensor<1x384x128xf32>
+ %3226 = "mhlo.broadcast_in_dim"(%2095) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3227 = mhlo.add %3225, %3226 : tensor<1x384x128xf32>
+ %3228 = "mhlo.reshape"(%3227) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3229 = "mhlo.dot"(%3228, %2114) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3230 = "mhlo.broadcast_in_dim"(%2113) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3231 = mhlo.add %3229, %3230 : tensor<384x512xf32>
+ %3232 = "mhlo.reshape"(%3231) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3233 = mhlo.maximum %3232, %1119 : tensor<1x384x512xf32>
+ %3234 = "mhlo.reshape"(%3233) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3235 = "mhlo.dot"(%3234, %2118) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3236 = "mhlo.broadcast_in_dim"(%2117) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3237 = mhlo.add %3235, %3236 : tensor<384x128xf32>
+ %3238 = "mhlo.reshape"(%3237) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3239 = mhlo.add %3238, %3227 : tensor<1x384x128xf32>
+ %3240 = "mhlo.broadcast_in_dim"(%2116) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3241 = mhlo.multiply %3239, %3240 : tensor<1x384x128xf32>
+ %3242 = "mhlo.broadcast_in_dim"(%2115) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3243 = mhlo.add %3241, %3242 : tensor<1x384x128xf32>
+ %3244 = "mhlo.reshape"(%3243) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3245 = "mhlo.dot"(%3244, %2120) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3246 = "mhlo.broadcast_in_dim"(%2119) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3247 = mhlo.add %3245, %3246 : tensor<384x512xf32>
+ %3248 = "mhlo.reshape"(%3247) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3249 = mhlo.maximum %3248, %1119 : tensor<1x384x512xf32>
+ %3250 = "mhlo.reshape"(%3249) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3251 = "mhlo.dot"(%3250, %2124) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3252 = "mhlo.broadcast_in_dim"(%2123) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3253 = mhlo.add %3251, %3252 : tensor<384x128xf32>
+ %3254 = "mhlo.reshape"(%3253) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3255 = mhlo.add %3254, %3243 : tensor<1x384x128xf32>
+ %3256 = "mhlo.broadcast_in_dim"(%2122) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3257 = mhlo.multiply %3255, %3256 : tensor<1x384x128xf32>
+ %3258 = "mhlo.broadcast_in_dim"(%2121) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3259 = mhlo.add %3257, %3258 : tensor<1x384x128xf32>
+ %3260 = "mhlo.reshape"(%3259) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3261 = "mhlo.dot"(%3260, %2126) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3262 = "mhlo.broadcast_in_dim"(%2125) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3263 = mhlo.add %3261, %3262 : tensor<384x512xf32>
+ %3264 = "mhlo.reshape"(%3263) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3265 = mhlo.maximum %3264, %1119 : tensor<1x384x512xf32>
+ %3266 = "mhlo.reshape"(%3265) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3267 = "mhlo.dot"(%3266, %2130) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3268 = "mhlo.broadcast_in_dim"(%2129) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3269 = mhlo.add %3267, %3268 : tensor<384x128xf32>
+ %3270 = "mhlo.reshape"(%3269) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3271 = mhlo.add %3270, %3259 : tensor<1x384x128xf32>
+ %3272 = "mhlo.broadcast_in_dim"(%2128) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3273 = mhlo.multiply %3271, %3272 : tensor<1x384x128xf32>
+ %3274 = "mhlo.broadcast_in_dim"(%2127) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3275 = mhlo.add %3273, %3274 : tensor<1x384x128xf32>
+ %3276 = "mhlo.reshape"(%3275) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3277 = "mhlo.dot"(%3276, %2132) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3278 = "mhlo.broadcast_in_dim"(%2131) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3279 = mhlo.add %3277, %3278 : tensor<384x512xf32>
+ %3280 = "mhlo.reshape"(%3279) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3281 = mhlo.maximum %3280, %1119 : tensor<1x384x512xf32>
+ %3282 = "mhlo.reshape"(%3281) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3283 = "mhlo.dot"(%3282, %2140) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3284 = "mhlo.broadcast_in_dim"(%2139) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3285 = mhlo.add %3283, %3284 : tensor<384x128xf32>
+ %3286 = "mhlo.reshape"(%3285) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3287 = mhlo.add %3286, %3275 : tensor<1x384x128xf32>
+ %3288 = "mhlo.broadcast_in_dim"(%2134) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3289 = mhlo.multiply %3287, %3288 : tensor<1x384x128xf32>
+ %3290 = "mhlo.broadcast_in_dim"(%2133) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3291 = mhlo.add %3289, %3290 : tensor<1x384x128xf32>
+ %3292 = "mhlo.reshape"(%3291) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3293 = "mhlo.dot"(%3292, %2138) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3294 = "mhlo.broadcast_in_dim"(%2137) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3295 = mhlo.add %3293, %3294 : tensor<384x512xf32>
+ %3296 = "mhlo.reshape"(%3295) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3297 = mhlo.add %3296, %3171 : tensor<1x384x512xf32>
+ %3298 = "mhlo.broadcast_in_dim"(%2136) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3299 = mhlo.multiply %3297, %3298 : tensor<1x384x512xf32>
+ %3300 = "mhlo.broadcast_in_dim"(%2135) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3301 = mhlo.add %3299, %3300 : tensor<1x384x512xf32>
+ %3302 = "mhlo.reshape"(%3301) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3303 = "mhlo.dot"(%3302, %2150) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3304 = "mhlo.broadcast_in_dim"(%2149) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3305 = mhlo.add %3303, %3304 : tensor<384x128xf32>
+ %3306 = "mhlo.reshape"(%3305) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3307 = "mhlo.transpose"(%3306) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3308 = "mhlo.dot"(%3302, %2154) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3309 = "mhlo.reshape"(%3308) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3310 = "mhlo.broadcast_in_dim"(%2153) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3311 = mhlo.add %3309, %3310 : tensor<1x384x128xf32>
+ %3312 = "mhlo.broadcast_in_dim"(%2152) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3313 = mhlo.multiply %3311, %3312 : tensor<1x384x128xf32>
+ %3314 = "mhlo.broadcast_in_dim"(%2151) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3315 = mhlo.add %3313, %3314 : tensor<1x384x128xf32>
+ %3316 = "mhlo.reshape"(%3315) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3317 = "mhlo.dot"(%3316, %2146) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3318 = "mhlo.broadcast_in_dim"(%2145) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3319 = mhlo.add %3317, %3318 : tensor<384x128xf32>
+ %3320 = "mhlo.reshape"(%3319) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3321 = "mhlo.transpose"(%3320) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3322 = "mhlo.dot"(%3316, %2148) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3323 = "mhlo.broadcast_in_dim"(%2147) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3324 = mhlo.add %3322, %3323 : tensor<384x128xf32>
+ %3325 = "mhlo.reshape"(%3324) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3326 = "mhlo.transpose"(%3325) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3327 = "mhlo.dot_general"(%3326, %3321) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3328 = mhlo.multiply %3327, %1114 : tensor<1x4x384x384xf32>
+ %3329 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3330 = mhlo.add %3328, %3329 : tensor<1x4x384x384xf32>
+ %3331 = "mhlo.reduce"(%3330, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3332 = "mhlo.broadcast_in_dim"(%3331) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3333 = mhlo.subtract %3330, %3332 : tensor<1x4x384x384xf32>
+ %3334 = "mhlo.exponential"(%3333) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3335 = "mhlo.reduce"(%3334, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3336 = "mhlo.broadcast_in_dim"(%3335) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3337 = mhlo.divide %3334, %3336 : tensor<1x4x384x384xf32>
+ %3338 = "mhlo.dot_general"(%3337, %3307) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3339 = "mhlo.transpose"(%3338) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3340 = "mhlo.reshape"(%3339) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3341 = "mhlo.dot"(%3340, %2144) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3342 = "mhlo.broadcast_in_dim"(%2143) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3343 = mhlo.add %3341, %3342 : tensor<384x128xf32>
+ %3344 = "mhlo.reshape"(%3343) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3345 = "mhlo.dot"(%3302, %2158) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3346 = "mhlo.broadcast_in_dim"(%2157) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3347 = mhlo.add %3345, %3346 : tensor<384x128xf32>
+ %3348 = "mhlo.reshape"(%3347) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3349 = "mhlo.broadcast_in_dim"(%2156) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3350 = mhlo.multiply %3348, %3349 : tensor<1x384x128xf32>
+ %3351 = "mhlo.broadcast_in_dim"(%2155) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3352 = mhlo.add %3350, %3351 : tensor<1x384x128xf32>
+ %3353 = mhlo.add %3344, %3352 : tensor<1x384x128xf32>
+ %3354 = "mhlo.broadcast_in_dim"(%2142) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3355 = mhlo.multiply %3353, %3354 : tensor<1x384x128xf32>
+ %3356 = "mhlo.broadcast_in_dim"(%2141) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3357 = mhlo.add %3355, %3356 : tensor<1x384x128xf32>
+ %3358 = "mhlo.reshape"(%3357) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3359 = "mhlo.dot"(%3358, %2160) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3360 = "mhlo.broadcast_in_dim"(%2159) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3361 = mhlo.add %3359, %3360 : tensor<384x512xf32>
+ %3362 = "mhlo.reshape"(%3361) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3363 = mhlo.maximum %3362, %1119 : tensor<1x384x512xf32>
+ %3364 = "mhlo.reshape"(%3363) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3365 = "mhlo.dot"(%3364, %2164) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3366 = "mhlo.broadcast_in_dim"(%2163) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3367 = mhlo.add %3365, %3366 : tensor<384x128xf32>
+ %3368 = "mhlo.reshape"(%3367) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3369 = mhlo.add %3368, %3357 : tensor<1x384x128xf32>
+ %3370 = "mhlo.broadcast_in_dim"(%2162) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3371 = mhlo.multiply %3369, %3370 : tensor<1x384x128xf32>
+ %3372 = "mhlo.broadcast_in_dim"(%2161) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3373 = mhlo.add %3371, %3372 : tensor<1x384x128xf32>
+ %3374 = "mhlo.reshape"(%3373) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3375 = "mhlo.dot"(%3374, %2166) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3376 = "mhlo.broadcast_in_dim"(%2165) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3377 = mhlo.add %3375, %3376 : tensor<384x512xf32>
+ %3378 = "mhlo.reshape"(%3377) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3379 = mhlo.maximum %3378, %1119 : tensor<1x384x512xf32>
+ %3380 = "mhlo.reshape"(%3379) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3381 = "mhlo.dot"(%3380, %2170) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3382 = "mhlo.broadcast_in_dim"(%2169) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3383 = mhlo.add %3381, %3382 : tensor<384x128xf32>
+ %3384 = "mhlo.reshape"(%3383) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3385 = mhlo.add %3384, %3373 : tensor<1x384x128xf32>
+ %3386 = "mhlo.broadcast_in_dim"(%2168) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3387 = mhlo.multiply %3385, %3386 : tensor<1x384x128xf32>
+ %3388 = "mhlo.broadcast_in_dim"(%2167) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3389 = mhlo.add %3387, %3388 : tensor<1x384x128xf32>
+ %3390 = "mhlo.reshape"(%3389) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3391 = "mhlo.dot"(%3390, %2172) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3392 = "mhlo.broadcast_in_dim"(%2171) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3393 = mhlo.add %3391, %3392 : tensor<384x512xf32>
+ %3394 = "mhlo.reshape"(%3393) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3395 = mhlo.maximum %3394, %1119 : tensor<1x384x512xf32>
+ %3396 = "mhlo.reshape"(%3395) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3397 = "mhlo.dot"(%3396, %2176) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3398 = "mhlo.broadcast_in_dim"(%2175) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3399 = mhlo.add %3397, %3398 : tensor<384x128xf32>
+ %3400 = "mhlo.reshape"(%3399) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3401 = mhlo.add %3400, %3389 : tensor<1x384x128xf32>
+ %3402 = "mhlo.broadcast_in_dim"(%2174) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3403 = mhlo.multiply %3401, %3402 : tensor<1x384x128xf32>
+ %3404 = "mhlo.broadcast_in_dim"(%2173) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3405 = mhlo.add %3403, %3404 : tensor<1x384x128xf32>
+ %3406 = "mhlo.reshape"(%3405) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3407 = "mhlo.dot"(%3406, %2178) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3408 = "mhlo.broadcast_in_dim"(%2177) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3409 = mhlo.add %3407, %3408 : tensor<384x512xf32>
+ %3410 = "mhlo.reshape"(%3409) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3411 = mhlo.maximum %3410, %1119 : tensor<1x384x512xf32>
+ %3412 = "mhlo.reshape"(%3411) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3413 = "mhlo.dot"(%3412, %2186) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3414 = "mhlo.broadcast_in_dim"(%2185) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3415 = mhlo.add %3413, %3414 : tensor<384x128xf32>
+ %3416 = "mhlo.reshape"(%3415) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3417 = mhlo.add %3416, %3405 : tensor<1x384x128xf32>
+ %3418 = "mhlo.broadcast_in_dim"(%2180) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3419 = mhlo.multiply %3417, %3418 : tensor<1x384x128xf32>
+ %3420 = "mhlo.broadcast_in_dim"(%2179) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3421 = mhlo.add %3419, %3420 : tensor<1x384x128xf32>
+ %3422 = "mhlo.reshape"(%3421) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3423 = "mhlo.dot"(%3422, %2184) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3424 = "mhlo.broadcast_in_dim"(%2183) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3425 = mhlo.add %3423, %3424 : tensor<384x512xf32>
+ %3426 = "mhlo.reshape"(%3425) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3427 = mhlo.add %3426, %3301 : tensor<1x384x512xf32>
+ %3428 = "mhlo.broadcast_in_dim"(%2182) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3429 = mhlo.multiply %3427, %3428 : tensor<1x384x512xf32>
+ %3430 = "mhlo.broadcast_in_dim"(%2181) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3431 = mhlo.add %3429, %3430 : tensor<1x384x512xf32>
+ %3432 = "mhlo.reshape"(%3431) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3433 = "mhlo.dot"(%3432, %2196) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3434 = "mhlo.broadcast_in_dim"(%2195) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3435 = mhlo.add %3433, %3434 : tensor<384x128xf32>
+ %3436 = "mhlo.reshape"(%3435) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3437 = "mhlo.transpose"(%3436) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3438 = "mhlo.dot"(%3432, %2200) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3439 = "mhlo.reshape"(%3438) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3440 = "mhlo.broadcast_in_dim"(%2199) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3441 = mhlo.add %3439, %3440 : tensor<1x384x128xf32>
+ %3442 = "mhlo.broadcast_in_dim"(%2198) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3443 = mhlo.multiply %3441, %3442 : tensor<1x384x128xf32>
+ %3444 = "mhlo.broadcast_in_dim"(%2197) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3445 = mhlo.add %3443, %3444 : tensor<1x384x128xf32>
+ %3446 = "mhlo.reshape"(%3445) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3447 = "mhlo.dot"(%3446, %2192) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3448 = "mhlo.broadcast_in_dim"(%2191) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3449 = mhlo.add %3447, %3448 : tensor<384x128xf32>
+ %3450 = "mhlo.reshape"(%3449) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3451 = "mhlo.transpose"(%3450) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3452 = "mhlo.dot"(%3446, %2194) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3453 = "mhlo.broadcast_in_dim"(%2193) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3454 = mhlo.add %3452, %3453 : tensor<384x128xf32>
+ %3455 = "mhlo.reshape"(%3454) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3456 = "mhlo.transpose"(%3455) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3457 = "mhlo.dot_general"(%3456, %3451) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3458 = mhlo.multiply %3457, %1114 : tensor<1x4x384x384xf32>
+ %3459 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3460 = mhlo.add %3458, %3459 : tensor<1x4x384x384xf32>
+ %3461 = "mhlo.reduce"(%3460, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3462 = "mhlo.broadcast_in_dim"(%3461) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3463 = mhlo.subtract %3460, %3462 : tensor<1x4x384x384xf32>
+ %3464 = "mhlo.exponential"(%3463) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3465 = "mhlo.reduce"(%3464, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3466 = "mhlo.broadcast_in_dim"(%3465) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3467 = mhlo.divide %3464, %3466 : tensor<1x4x384x384xf32>
+ %3468 = "mhlo.dot_general"(%3467, %3437) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3469 = "mhlo.transpose"(%3468) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3470 = "mhlo.reshape"(%3469) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3471 = "mhlo.dot"(%3470, %2190) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3472 = "mhlo.broadcast_in_dim"(%2189) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3473 = mhlo.add %3471, %3472 : tensor<384x128xf32>
+ %3474 = "mhlo.reshape"(%3473) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3475 = "mhlo.dot"(%3432, %2204) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3476 = "mhlo.broadcast_in_dim"(%2203) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3477 = mhlo.add %3475, %3476 : tensor<384x128xf32>
+ %3478 = "mhlo.reshape"(%3477) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3479 = "mhlo.broadcast_in_dim"(%2202) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3480 = mhlo.multiply %3478, %3479 : tensor<1x384x128xf32>
+ %3481 = "mhlo.broadcast_in_dim"(%2201) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3482 = mhlo.add %3480, %3481 : tensor<1x384x128xf32>
+ %3483 = mhlo.add %3474, %3482 : tensor<1x384x128xf32>
+ %3484 = "mhlo.broadcast_in_dim"(%2188) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3485 = mhlo.multiply %3483, %3484 : tensor<1x384x128xf32>
+ %3486 = "mhlo.broadcast_in_dim"(%2187) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3487 = mhlo.add %3485, %3486 : tensor<1x384x128xf32>
+ %3488 = "mhlo.reshape"(%3487) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3489 = "mhlo.dot"(%3488, %2206) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3490 = "mhlo.broadcast_in_dim"(%2205) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3491 = mhlo.add %3489, %3490 : tensor<384x512xf32>
+ %3492 = "mhlo.reshape"(%3491) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3493 = mhlo.maximum %3492, %1119 : tensor<1x384x512xf32>
+ %3494 = "mhlo.reshape"(%3493) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3495 = "mhlo.dot"(%3494, %2210) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3496 = "mhlo.broadcast_in_dim"(%2209) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3497 = mhlo.add %3495, %3496 : tensor<384x128xf32>
+ %3498 = "mhlo.reshape"(%3497) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3499 = mhlo.add %3498, %3487 : tensor<1x384x128xf32>
+ %3500 = "mhlo.broadcast_in_dim"(%2208) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3501 = mhlo.multiply %3499, %3500 : tensor<1x384x128xf32>
+ %3502 = "mhlo.broadcast_in_dim"(%2207) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3503 = mhlo.add %3501, %3502 : tensor<1x384x128xf32>
+ %3504 = "mhlo.reshape"(%3503) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3505 = "mhlo.dot"(%3504, %2212) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3506 = "mhlo.broadcast_in_dim"(%2211) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3507 = mhlo.add %3505, %3506 : tensor<384x512xf32>
+ %3508 = "mhlo.reshape"(%3507) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3509 = mhlo.maximum %3508, %1119 : tensor<1x384x512xf32>
+ %3510 = "mhlo.reshape"(%3509) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3511 = "mhlo.dot"(%3510, %2216) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3512 = "mhlo.broadcast_in_dim"(%2215) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3513 = mhlo.add %3511, %3512 : tensor<384x128xf32>
+ %3514 = "mhlo.reshape"(%3513) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3515 = mhlo.add %3514, %3503 : tensor<1x384x128xf32>
+ %3516 = "mhlo.broadcast_in_dim"(%2214) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3517 = mhlo.multiply %3515, %3516 : tensor<1x384x128xf32>
+ %3518 = "mhlo.broadcast_in_dim"(%2213) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3519 = mhlo.add %3517, %3518 : tensor<1x384x128xf32>
+ %3520 = "mhlo.reshape"(%3519) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3521 = "mhlo.dot"(%3520, %2218) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3522 = "mhlo.broadcast_in_dim"(%2217) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3523 = mhlo.add %3521, %3522 : tensor<384x512xf32>
+ %3524 = "mhlo.reshape"(%3523) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3525 = mhlo.maximum %3524, %1119 : tensor<1x384x512xf32>
+ %3526 = "mhlo.reshape"(%3525) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3527 = "mhlo.dot"(%3526, %2222) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3528 = "mhlo.broadcast_in_dim"(%2221) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3529 = mhlo.add %3527, %3528 : tensor<384x128xf32>
+ %3530 = "mhlo.reshape"(%3529) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3531 = mhlo.add %3530, %3519 : tensor<1x384x128xf32>
+ %3532 = "mhlo.broadcast_in_dim"(%2220) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3533 = mhlo.multiply %3531, %3532 : tensor<1x384x128xf32>
+ %3534 = "mhlo.broadcast_in_dim"(%2219) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3535 = mhlo.add %3533, %3534 : tensor<1x384x128xf32>
+ %3536 = "mhlo.reshape"(%3535) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3537 = "mhlo.dot"(%3536, %2224) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3538 = "mhlo.broadcast_in_dim"(%2223) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3539 = mhlo.add %3537, %3538 : tensor<384x512xf32>
+ %3540 = "mhlo.reshape"(%3539) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3541 = mhlo.maximum %3540, %1119 : tensor<1x384x512xf32>
+ %3542 = "mhlo.reshape"(%3541) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3543 = "mhlo.dot"(%3542, %2232) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3544 = "mhlo.broadcast_in_dim"(%2231) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3545 = mhlo.add %3543, %3544 : tensor<384x128xf32>
+ %3546 = "mhlo.reshape"(%3545) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3547 = mhlo.add %3546, %3535 : tensor<1x384x128xf32>
+ %3548 = "mhlo.broadcast_in_dim"(%2226) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3549 = mhlo.multiply %3547, %3548 : tensor<1x384x128xf32>
+ %3550 = "mhlo.broadcast_in_dim"(%2225) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3551 = mhlo.add %3549, %3550 : tensor<1x384x128xf32>
+ %3552 = "mhlo.reshape"(%3551) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3553 = "mhlo.dot"(%3552, %2230) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3554 = "mhlo.broadcast_in_dim"(%2229) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3555 = mhlo.add %3553, %3554 : tensor<384x512xf32>
+ %3556 = "mhlo.reshape"(%3555) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3557 = mhlo.add %3556, %3431 : tensor<1x384x512xf32>
+ %3558 = "mhlo.broadcast_in_dim"(%2228) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3559 = mhlo.multiply %3557, %3558 : tensor<1x384x512xf32>
+ %3560 = "mhlo.broadcast_in_dim"(%2227) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3561 = mhlo.add %3559, %3560 : tensor<1x384x512xf32>
+ %3562 = "mhlo.reshape"(%3561) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3563 = "mhlo.dot"(%3562, %1230) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3564 = "mhlo.broadcast_in_dim"(%1229) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3565 = mhlo.add %3563, %3564 : tensor<384x128xf32>
+ %3566 = "mhlo.reshape"(%3565) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3567 = "mhlo.transpose"(%3566) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3568 = "mhlo.dot"(%3562, %1234) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3569 = "mhlo.reshape"(%3568) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3570 = "mhlo.broadcast_in_dim"(%1233) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3571 = mhlo.add %3569, %3570 : tensor<1x384x128xf32>
+ %3572 = "mhlo.broadcast_in_dim"(%1232) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3573 = mhlo.multiply %3571, %3572 : tensor<1x384x128xf32>
+ %3574 = "mhlo.broadcast_in_dim"(%1231) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3575 = mhlo.add %3573, %3574 : tensor<1x384x128xf32>
+ %3576 = "mhlo.reshape"(%3575) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3577 = "mhlo.dot"(%3576, %1226) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3578 = "mhlo.broadcast_in_dim"(%1225) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3579 = mhlo.add %3577, %3578 : tensor<384x128xf32>
+ %3580 = "mhlo.reshape"(%3579) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3581 = "mhlo.transpose"(%3580) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3582 = "mhlo.dot"(%3576, %1228) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3583 = "mhlo.broadcast_in_dim"(%1227) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3584 = mhlo.add %3582, %3583 : tensor<384x128xf32>
+ %3585 = "mhlo.reshape"(%3584) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3586 = "mhlo.transpose"(%3585) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3587 = "mhlo.dot_general"(%3586, %3581) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3588 = mhlo.multiply %3587, %1114 : tensor<1x4x384x384xf32>
+ %3589 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3590 = mhlo.add %3588, %3589 : tensor<1x4x384x384xf32>
+ %3591 = "mhlo.reduce"(%3590, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3592 = "mhlo.broadcast_in_dim"(%3591) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3593 = mhlo.subtract %3590, %3592 : tensor<1x4x384x384xf32>
+ %3594 = "mhlo.exponential"(%3593) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3595 = "mhlo.reduce"(%3594, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3596 = "mhlo.broadcast_in_dim"(%3595) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3597 = mhlo.divide %3594, %3596 : tensor<1x4x384x384xf32>
+ %3598 = "mhlo.dot_general"(%3597, %3567) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3599 = "mhlo.transpose"(%3598) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3600 = "mhlo.reshape"(%3599) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3601 = "mhlo.dot"(%3600, %1224) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3602 = "mhlo.broadcast_in_dim"(%1223) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3603 = mhlo.add %3601, %3602 : tensor<384x128xf32>
+ %3604 = "mhlo.reshape"(%3603) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3605 = "mhlo.dot"(%3562, %1238) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3606 = "mhlo.broadcast_in_dim"(%1237) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3607 = mhlo.add %3605, %3606 : tensor<384x128xf32>
+ %3608 = "mhlo.reshape"(%3607) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3609 = "mhlo.broadcast_in_dim"(%1236) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3610 = mhlo.multiply %3608, %3609 : tensor<1x384x128xf32>
+ %3611 = "mhlo.broadcast_in_dim"(%1235) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3612 = mhlo.add %3610, %3611 : tensor<1x384x128xf32>
+ %3613 = mhlo.add %3604, %3612 : tensor<1x384x128xf32>
+ %3614 = "mhlo.broadcast_in_dim"(%1222) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3615 = mhlo.multiply %3613, %3614 : tensor<1x384x128xf32>
+ %3616 = "mhlo.broadcast_in_dim"(%1221) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3617 = mhlo.add %3615, %3616 : tensor<1x384x128xf32>
+ %3618 = "mhlo.reshape"(%3617) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3619 = "mhlo.dot"(%3618, %1240) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3620 = "mhlo.broadcast_in_dim"(%1239) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3621 = mhlo.add %3619, %3620 : tensor<384x512xf32>
+ %3622 = "mhlo.reshape"(%3621) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3623 = mhlo.maximum %3622, %1119 : tensor<1x384x512xf32>
+ %3624 = "mhlo.reshape"(%3623) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3625 = "mhlo.dot"(%3624, %1244) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3626 = "mhlo.broadcast_in_dim"(%1243) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3627 = mhlo.add %3625, %3626 : tensor<384x128xf32>
+ %3628 = "mhlo.reshape"(%3627) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3629 = mhlo.add %3628, %3617 : tensor<1x384x128xf32>
+ %3630 = "mhlo.broadcast_in_dim"(%1242) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3631 = mhlo.multiply %3629, %3630 : tensor<1x384x128xf32>
+ %3632 = "mhlo.broadcast_in_dim"(%1241) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3633 = mhlo.add %3631, %3632 : tensor<1x384x128xf32>
+ %3634 = "mhlo.reshape"(%3633) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3635 = "mhlo.dot"(%3634, %1246) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3636 = "mhlo.broadcast_in_dim"(%1245) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3637 = mhlo.add %3635, %3636 : tensor<384x512xf32>
+ %3638 = "mhlo.reshape"(%3637) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3639 = mhlo.maximum %3638, %1119 : tensor<1x384x512xf32>
+ %3640 = "mhlo.reshape"(%3639) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3641 = "mhlo.dot"(%3640, %1250) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3642 = "mhlo.broadcast_in_dim"(%1249) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3643 = mhlo.add %3641, %3642 : tensor<384x128xf32>
+ %3644 = "mhlo.reshape"(%3643) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3645 = mhlo.add %3644, %3633 : tensor<1x384x128xf32>
+ %3646 = "mhlo.broadcast_in_dim"(%1248) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3647 = mhlo.multiply %3645, %3646 : tensor<1x384x128xf32>
+ %3648 = "mhlo.broadcast_in_dim"(%1247) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3649 = mhlo.add %3647, %3648 : tensor<1x384x128xf32>
+ %3650 = "mhlo.reshape"(%3649) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3651 = "mhlo.dot"(%3650, %1252) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3652 = "mhlo.broadcast_in_dim"(%1251) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3653 = mhlo.add %3651, %3652 : tensor<384x512xf32>
+ %3654 = "mhlo.reshape"(%3653) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3655 = mhlo.maximum %3654, %1119 : tensor<1x384x512xf32>
+ %3656 = "mhlo.reshape"(%3655) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3657 = "mhlo.dot"(%3656, %1256) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3658 = "mhlo.broadcast_in_dim"(%1255) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3659 = mhlo.add %3657, %3658 : tensor<384x128xf32>
+ %3660 = "mhlo.reshape"(%3659) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3661 = mhlo.add %3660, %3649 : tensor<1x384x128xf32>
+ %3662 = "mhlo.broadcast_in_dim"(%1254) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3663 = mhlo.multiply %3661, %3662 : tensor<1x384x128xf32>
+ %3664 = "mhlo.broadcast_in_dim"(%1253) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3665 = mhlo.add %3663, %3664 : tensor<1x384x128xf32>
+ %3666 = "mhlo.reshape"(%3665) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3667 = "mhlo.dot"(%3666, %1258) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3668 = "mhlo.broadcast_in_dim"(%1257) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3669 = mhlo.add %3667, %3668 : tensor<384x512xf32>
+ %3670 = "mhlo.reshape"(%3669) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3671 = mhlo.maximum %3670, %1119 : tensor<1x384x512xf32>
+ %3672 = "mhlo.reshape"(%3671) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3673 = "mhlo.dot"(%3672, %1266) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3674 = "mhlo.broadcast_in_dim"(%1265) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3675 = mhlo.add %3673, %3674 : tensor<384x128xf32>
+ %3676 = "mhlo.reshape"(%3675) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3677 = mhlo.add %3676, %3665 : tensor<1x384x128xf32>
+ %3678 = "mhlo.broadcast_in_dim"(%1260) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3679 = mhlo.multiply %3677, %3678 : tensor<1x384x128xf32>
+ %3680 = "mhlo.broadcast_in_dim"(%1259) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3681 = mhlo.add %3679, %3680 : tensor<1x384x128xf32>
+ %3682 = "mhlo.reshape"(%3681) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3683 = "mhlo.dot"(%3682, %1264) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3684 = "mhlo.broadcast_in_dim"(%1263) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3685 = mhlo.add %3683, %3684 : tensor<384x512xf32>
+ %3686 = "mhlo.reshape"(%3685) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3687 = mhlo.add %3686, %3561 : tensor<1x384x512xf32>
+ %3688 = "mhlo.broadcast_in_dim"(%1262) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3689 = mhlo.multiply %3687, %3688 : tensor<1x384x512xf32>
+ %3690 = "mhlo.broadcast_in_dim"(%1261) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3691 = mhlo.add %3689, %3690 : tensor<1x384x512xf32>
+ %3692 = "mhlo.reshape"(%3691) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3693 = "mhlo.dot"(%3692, %1276) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3694 = "mhlo.broadcast_in_dim"(%1275) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3695 = mhlo.add %3693, %3694 : tensor<384x128xf32>
+ %3696 = "mhlo.reshape"(%3695) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3697 = "mhlo.transpose"(%3696) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3698 = "mhlo.dot"(%3692, %1280) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3699 = "mhlo.reshape"(%3698) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3700 = "mhlo.broadcast_in_dim"(%1279) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3701 = mhlo.add %3699, %3700 : tensor<1x384x128xf32>
+ %3702 = "mhlo.broadcast_in_dim"(%1278) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3703 = mhlo.multiply %3701, %3702 : tensor<1x384x128xf32>
+ %3704 = "mhlo.broadcast_in_dim"(%1277) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3705 = mhlo.add %3703, %3704 : tensor<1x384x128xf32>
+ %3706 = "mhlo.reshape"(%3705) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3707 = "mhlo.dot"(%3706, %1272) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3708 = "mhlo.broadcast_in_dim"(%1271) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3709 = mhlo.add %3707, %3708 : tensor<384x128xf32>
+ %3710 = "mhlo.reshape"(%3709) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3711 = "mhlo.transpose"(%3710) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3712 = "mhlo.dot"(%3706, %1274) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3713 = "mhlo.broadcast_in_dim"(%1273) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3714 = mhlo.add %3712, %3713 : tensor<384x128xf32>
+ %3715 = "mhlo.reshape"(%3714) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3716 = "mhlo.transpose"(%3715) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3717 = "mhlo.dot_general"(%3716, %3711) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3718 = mhlo.multiply %3717, %1114 : tensor<1x4x384x384xf32>
+ %3719 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3720 = mhlo.add %3718, %3719 : tensor<1x4x384x384xf32>
+ %3721 = "mhlo.reduce"(%3720, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3722 = "mhlo.broadcast_in_dim"(%3721) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3723 = mhlo.subtract %3720, %3722 : tensor<1x4x384x384xf32>
+ %3724 = "mhlo.exponential"(%3723) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3725 = "mhlo.reduce"(%3724, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3726 = "mhlo.broadcast_in_dim"(%3725) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3727 = mhlo.divide %3724, %3726 : tensor<1x4x384x384xf32>
+ %3728 = "mhlo.dot_general"(%3727, %3697) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3729 = "mhlo.transpose"(%3728) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3730 = "mhlo.reshape"(%3729) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3731 = "mhlo.dot"(%3730, %1270) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3732 = "mhlo.broadcast_in_dim"(%1269) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3733 = mhlo.add %3731, %3732 : tensor<384x128xf32>
+ %3734 = "mhlo.reshape"(%3733) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3735 = "mhlo.dot"(%3692, %1284) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3736 = "mhlo.broadcast_in_dim"(%1283) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3737 = mhlo.add %3735, %3736 : tensor<384x128xf32>
+ %3738 = "mhlo.reshape"(%3737) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3739 = "mhlo.broadcast_in_dim"(%1282) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3740 = mhlo.multiply %3738, %3739 : tensor<1x384x128xf32>
+ %3741 = "mhlo.broadcast_in_dim"(%1281) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3742 = mhlo.add %3740, %3741 : tensor<1x384x128xf32>
+ %3743 = mhlo.add %3734, %3742 : tensor<1x384x128xf32>
+ %3744 = "mhlo.broadcast_in_dim"(%1268) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3745 = mhlo.multiply %3743, %3744 : tensor<1x384x128xf32>
+ %3746 = "mhlo.broadcast_in_dim"(%1267) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3747 = mhlo.add %3745, %3746 : tensor<1x384x128xf32>
+ %3748 = "mhlo.reshape"(%3747) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3749 = "mhlo.dot"(%3748, %1286) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3750 = "mhlo.broadcast_in_dim"(%1285) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3751 = mhlo.add %3749, %3750 : tensor<384x512xf32>
+ %3752 = "mhlo.reshape"(%3751) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3753 = mhlo.maximum %3752, %1119 : tensor<1x384x512xf32>
+ %3754 = "mhlo.reshape"(%3753) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3755 = "mhlo.dot"(%3754, %1290) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3756 = "mhlo.broadcast_in_dim"(%1289) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3757 = mhlo.add %3755, %3756 : tensor<384x128xf32>
+ %3758 = "mhlo.reshape"(%3757) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3759 = mhlo.add %3758, %3747 : tensor<1x384x128xf32>
+ %3760 = "mhlo.broadcast_in_dim"(%1288) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3761 = mhlo.multiply %3759, %3760 : tensor<1x384x128xf32>
+ %3762 = "mhlo.broadcast_in_dim"(%1287) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3763 = mhlo.add %3761, %3762 : tensor<1x384x128xf32>
+ %3764 = "mhlo.reshape"(%3763) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3765 = "mhlo.dot"(%3764, %1292) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3766 = "mhlo.broadcast_in_dim"(%1291) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3767 = mhlo.add %3765, %3766 : tensor<384x512xf32>
+ %3768 = "mhlo.reshape"(%3767) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3769 = mhlo.maximum %3768, %1119 : tensor<1x384x512xf32>
+ %3770 = "mhlo.reshape"(%3769) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3771 = "mhlo.dot"(%3770, %1296) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3772 = "mhlo.broadcast_in_dim"(%1295) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3773 = mhlo.add %3771, %3772 : tensor<384x128xf32>
+ %3774 = "mhlo.reshape"(%3773) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3775 = mhlo.add %3774, %3763 : tensor<1x384x128xf32>
+ %3776 = "mhlo.broadcast_in_dim"(%1294) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3777 = mhlo.multiply %3775, %3776 : tensor<1x384x128xf32>
+ %3778 = "mhlo.broadcast_in_dim"(%1293) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3779 = mhlo.add %3777, %3778 : tensor<1x384x128xf32>
+ %3780 = "mhlo.reshape"(%3779) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3781 = "mhlo.dot"(%3780, %1298) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3782 = "mhlo.broadcast_in_dim"(%1297) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3783 = mhlo.add %3781, %3782 : tensor<384x512xf32>
+ %3784 = "mhlo.reshape"(%3783) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3785 = mhlo.maximum %3784, %1119 : tensor<1x384x512xf32>
+ %3786 = "mhlo.reshape"(%3785) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3787 = "mhlo.dot"(%3786, %1302) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3788 = "mhlo.broadcast_in_dim"(%1301) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3789 = mhlo.add %3787, %3788 : tensor<384x128xf32>
+ %3790 = "mhlo.reshape"(%3789) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3791 = mhlo.add %3790, %3779 : tensor<1x384x128xf32>
+ %3792 = "mhlo.broadcast_in_dim"(%1300) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3793 = mhlo.multiply %3791, %3792 : tensor<1x384x128xf32>
+ %3794 = "mhlo.broadcast_in_dim"(%1299) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3795 = mhlo.add %3793, %3794 : tensor<1x384x128xf32>
+ %3796 = "mhlo.reshape"(%3795) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3797 = "mhlo.dot"(%3796, %1304) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3798 = "mhlo.broadcast_in_dim"(%1303) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3799 = mhlo.add %3797, %3798 : tensor<384x512xf32>
+ %3800 = "mhlo.reshape"(%3799) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3801 = mhlo.maximum %3800, %1119 : tensor<1x384x512xf32>
+ %3802 = "mhlo.reshape"(%3801) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3803 = "mhlo.dot"(%3802, %1312) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3804 = "mhlo.broadcast_in_dim"(%1311) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3805 = mhlo.add %3803, %3804 : tensor<384x128xf32>
+ %3806 = "mhlo.reshape"(%3805) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3807 = mhlo.add %3806, %3795 : tensor<1x384x128xf32>
+ %3808 = "mhlo.broadcast_in_dim"(%1306) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3809 = mhlo.multiply %3807, %3808 : tensor<1x384x128xf32>
+ %3810 = "mhlo.broadcast_in_dim"(%1305) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3811 = mhlo.add %3809, %3810 : tensor<1x384x128xf32>
+ %3812 = "mhlo.reshape"(%3811) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3813 = "mhlo.dot"(%3812, %1310) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3814 = "mhlo.broadcast_in_dim"(%1309) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3815 = mhlo.add %3813, %3814 : tensor<384x512xf32>
+ %3816 = "mhlo.reshape"(%3815) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3817 = mhlo.add %3816, %3691 : tensor<1x384x512xf32>
+ %3818 = "mhlo.broadcast_in_dim"(%1308) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3819 = mhlo.multiply %3817, %3818 : tensor<1x384x512xf32>
+ %3820 = "mhlo.broadcast_in_dim"(%1307) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3821 = mhlo.add %3819, %3820 : tensor<1x384x512xf32>
+ %3822 = "mhlo.reshape"(%3821) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3823 = "mhlo.dot"(%3822, %1322) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3824 = "mhlo.broadcast_in_dim"(%1321) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3825 = mhlo.add %3823, %3824 : tensor<384x128xf32>
+ %3826 = "mhlo.reshape"(%3825) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3827 = "mhlo.transpose"(%3826) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3828 = "mhlo.dot"(%3822, %1326) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3829 = "mhlo.reshape"(%3828) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3830 = "mhlo.broadcast_in_dim"(%1325) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3831 = mhlo.add %3829, %3830 : tensor<1x384x128xf32>
+ %3832 = "mhlo.broadcast_in_dim"(%1324) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3833 = mhlo.multiply %3831, %3832 : tensor<1x384x128xf32>
+ %3834 = "mhlo.broadcast_in_dim"(%1323) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3835 = mhlo.add %3833, %3834 : tensor<1x384x128xf32>
+ %3836 = "mhlo.reshape"(%3835) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3837 = "mhlo.dot"(%3836, %1318) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3838 = "mhlo.broadcast_in_dim"(%1317) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3839 = mhlo.add %3837, %3838 : tensor<384x128xf32>
+ %3840 = "mhlo.reshape"(%3839) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3841 = "mhlo.transpose"(%3840) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3842 = "mhlo.dot"(%3836, %1320) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3843 = "mhlo.broadcast_in_dim"(%1319) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3844 = mhlo.add %3842, %3843 : tensor<384x128xf32>
+ %3845 = "mhlo.reshape"(%3844) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3846 = "mhlo.transpose"(%3845) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3847 = "mhlo.dot_general"(%3846, %3841) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3848 = mhlo.multiply %3847, %1114 : tensor<1x4x384x384xf32>
+ %3849 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3850 = mhlo.add %3848, %3849 : tensor<1x4x384x384xf32>
+ %3851 = "mhlo.reduce"(%3850, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3852 = "mhlo.broadcast_in_dim"(%3851) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3853 = mhlo.subtract %3850, %3852 : tensor<1x4x384x384xf32>
+ %3854 = "mhlo.exponential"(%3853) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3855 = "mhlo.reduce"(%3854, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3856 = "mhlo.broadcast_in_dim"(%3855) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3857 = mhlo.divide %3854, %3856 : tensor<1x4x384x384xf32>
+ %3858 = "mhlo.dot_general"(%3857, %3827) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3859 = "mhlo.transpose"(%3858) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3860 = "mhlo.reshape"(%3859) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3861 = "mhlo.dot"(%3860, %1316) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3862 = "mhlo.broadcast_in_dim"(%1315) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3863 = mhlo.add %3861, %3862 : tensor<384x128xf32>
+ %3864 = "mhlo.reshape"(%3863) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3865 = "mhlo.dot"(%3822, %1330) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3866 = "mhlo.broadcast_in_dim"(%1329) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3867 = mhlo.add %3865, %3866 : tensor<384x128xf32>
+ %3868 = "mhlo.reshape"(%3867) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3869 = "mhlo.broadcast_in_dim"(%1328) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3870 = mhlo.multiply %3868, %3869 : tensor<1x384x128xf32>
+ %3871 = "mhlo.broadcast_in_dim"(%1327) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3872 = mhlo.add %3870, %3871 : tensor<1x384x128xf32>
+ %3873 = mhlo.add %3864, %3872 : tensor<1x384x128xf32>
+ %3874 = "mhlo.broadcast_in_dim"(%1314) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3875 = mhlo.multiply %3873, %3874 : tensor<1x384x128xf32>
+ %3876 = "mhlo.broadcast_in_dim"(%1313) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3877 = mhlo.add %3875, %3876 : tensor<1x384x128xf32>
+ %3878 = "mhlo.reshape"(%3877) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3879 = "mhlo.dot"(%3878, %1332) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3880 = "mhlo.broadcast_in_dim"(%1331) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3881 = mhlo.add %3879, %3880 : tensor<384x512xf32>
+ %3882 = "mhlo.reshape"(%3881) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3883 = mhlo.maximum %3882, %1119 : tensor<1x384x512xf32>
+ %3884 = "mhlo.reshape"(%3883) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3885 = "mhlo.dot"(%3884, %1336) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3886 = "mhlo.broadcast_in_dim"(%1335) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3887 = mhlo.add %3885, %3886 : tensor<384x128xf32>
+ %3888 = "mhlo.reshape"(%3887) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3889 = mhlo.add %3888, %3877 : tensor<1x384x128xf32>
+ %3890 = "mhlo.broadcast_in_dim"(%1334) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3891 = mhlo.multiply %3889, %3890 : tensor<1x384x128xf32>
+ %3892 = "mhlo.broadcast_in_dim"(%1333) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3893 = mhlo.add %3891, %3892 : tensor<1x384x128xf32>
+ %3894 = "mhlo.reshape"(%3893) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3895 = "mhlo.dot"(%3894, %1338) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3896 = "mhlo.broadcast_in_dim"(%1337) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3897 = mhlo.add %3895, %3896 : tensor<384x512xf32>
+ %3898 = "mhlo.reshape"(%3897) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3899 = mhlo.maximum %3898, %1119 : tensor<1x384x512xf32>
+ %3900 = "mhlo.reshape"(%3899) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3901 = "mhlo.dot"(%3900, %1342) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3902 = "mhlo.broadcast_in_dim"(%1341) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3903 = mhlo.add %3901, %3902 : tensor<384x128xf32>
+ %3904 = "mhlo.reshape"(%3903) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3905 = mhlo.add %3904, %3893 : tensor<1x384x128xf32>
+ %3906 = "mhlo.broadcast_in_dim"(%1340) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3907 = mhlo.multiply %3905, %3906 : tensor<1x384x128xf32>
+ %3908 = "mhlo.broadcast_in_dim"(%1339) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3909 = mhlo.add %3907, %3908 : tensor<1x384x128xf32>
+ %3910 = "mhlo.reshape"(%3909) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3911 = "mhlo.dot"(%3910, %1344) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3912 = "mhlo.broadcast_in_dim"(%1343) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3913 = mhlo.add %3911, %3912 : tensor<384x512xf32>
+ %3914 = "mhlo.reshape"(%3913) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3915 = mhlo.maximum %3914, %1119 : tensor<1x384x512xf32>
+ %3916 = "mhlo.reshape"(%3915) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3917 = "mhlo.dot"(%3916, %1348) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3918 = "mhlo.broadcast_in_dim"(%1347) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3919 = mhlo.add %3917, %3918 : tensor<384x128xf32>
+ %3920 = "mhlo.reshape"(%3919) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3921 = mhlo.add %3920, %3909 : tensor<1x384x128xf32>
+ %3922 = "mhlo.broadcast_in_dim"(%1346) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3923 = mhlo.multiply %3921, %3922 : tensor<1x384x128xf32>
+ %3924 = "mhlo.broadcast_in_dim"(%1345) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3925 = mhlo.add %3923, %3924 : tensor<1x384x128xf32>
+ %3926 = "mhlo.reshape"(%3925) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3927 = "mhlo.dot"(%3926, %1350) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3928 = "mhlo.broadcast_in_dim"(%1349) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3929 = mhlo.add %3927, %3928 : tensor<384x512xf32>
+ %3930 = "mhlo.reshape"(%3929) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3931 = mhlo.maximum %3930, %1119 : tensor<1x384x512xf32>
+ %3932 = "mhlo.reshape"(%3931) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3933 = "mhlo.dot"(%3932, %1358) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3934 = "mhlo.broadcast_in_dim"(%1357) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3935 = mhlo.add %3933, %3934 : tensor<384x128xf32>
+ %3936 = "mhlo.reshape"(%3935) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3937 = mhlo.add %3936, %3925 : tensor<1x384x128xf32>
+ %3938 = "mhlo.broadcast_in_dim"(%1352) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3939 = mhlo.multiply %3937, %3938 : tensor<1x384x128xf32>
+ %3940 = "mhlo.broadcast_in_dim"(%1351) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3941 = mhlo.add %3939, %3940 : tensor<1x384x128xf32>
+ %3942 = "mhlo.reshape"(%3941) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3943 = "mhlo.dot"(%3942, %1356) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %3944 = "mhlo.broadcast_in_dim"(%1355) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %3945 = mhlo.add %3943, %3944 : tensor<384x512xf32>
+ %3946 = "mhlo.reshape"(%3945) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %3947 = mhlo.add %3946, %3821 : tensor<1x384x512xf32>
+ %3948 = "mhlo.broadcast_in_dim"(%1354) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3949 = mhlo.multiply %3947, %3948 : tensor<1x384x512xf32>
+ %3950 = "mhlo.broadcast_in_dim"(%1353) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %3951 = mhlo.add %3949, %3950 : tensor<1x384x512xf32>
+ %3952 = "mhlo.reshape"(%3951) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %3953 = "mhlo.dot"(%3952, %1368) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3954 = "mhlo.broadcast_in_dim"(%1367) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3955 = mhlo.add %3953, %3954 : tensor<384x128xf32>
+ %3956 = "mhlo.reshape"(%3955) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3957 = "mhlo.transpose"(%3956) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3958 = "mhlo.dot"(%3952, %1372) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3959 = "mhlo.reshape"(%3958) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3960 = "mhlo.broadcast_in_dim"(%1371) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3961 = mhlo.add %3959, %3960 : tensor<1x384x128xf32>
+ %3962 = "mhlo.broadcast_in_dim"(%1370) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3963 = mhlo.multiply %3961, %3962 : tensor<1x384x128xf32>
+ %3964 = "mhlo.broadcast_in_dim"(%1369) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %3965 = mhlo.add %3963, %3964 : tensor<1x384x128xf32>
+ %3966 = "mhlo.reshape"(%3965) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %3967 = "mhlo.dot"(%3966, %1364) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3968 = "mhlo.broadcast_in_dim"(%1363) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3969 = mhlo.add %3967, %3968 : tensor<384x128xf32>
+ %3970 = "mhlo.reshape"(%3969) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3971 = "mhlo.transpose"(%3970) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3972 = "mhlo.dot"(%3966, %1366) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3973 = "mhlo.broadcast_in_dim"(%1365) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3974 = mhlo.add %3972, %3973 : tensor<384x128xf32>
+ %3975 = "mhlo.reshape"(%3974) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %3976 = "mhlo.transpose"(%3975) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %3977 = "mhlo.dot_general"(%3976, %3971) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %3978 = mhlo.multiply %3977, %1114 : tensor<1x4x384x384xf32>
+ %3979 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3980 = mhlo.add %3978, %3979 : tensor<1x4x384x384xf32>
+ %3981 = "mhlo.reduce"(%3980, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3982 = "mhlo.broadcast_in_dim"(%3981) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3983 = mhlo.subtract %3980, %3982 : tensor<1x4x384x384xf32>
+ %3984 = "mhlo.exponential"(%3983) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %3985 = "mhlo.reduce"(%3984, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %3986 = "mhlo.broadcast_in_dim"(%3985) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %3987 = mhlo.divide %3984, %3986 : tensor<1x4x384x384xf32>
+ %3988 = "mhlo.dot_general"(%3987, %3957) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %3989 = "mhlo.transpose"(%3988) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %3990 = "mhlo.reshape"(%3989) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %3991 = "mhlo.dot"(%3990, %1362) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %3992 = "mhlo.broadcast_in_dim"(%1361) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3993 = mhlo.add %3991, %3992 : tensor<384x128xf32>
+ %3994 = "mhlo.reshape"(%3993) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3995 = "mhlo.dot"(%3952, %1376) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %3996 = "mhlo.broadcast_in_dim"(%1375) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %3997 = mhlo.add %3995, %3996 : tensor<384x128xf32>
+ %3998 = "mhlo.reshape"(%3997) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %3999 = "mhlo.broadcast_in_dim"(%1374) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4000 = mhlo.multiply %3998, %3999 : tensor<1x384x128xf32>
+ %4001 = "mhlo.broadcast_in_dim"(%1373) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4002 = mhlo.add %4000, %4001 : tensor<1x384x128xf32>
+ %4003 = mhlo.add %3994, %4002 : tensor<1x384x128xf32>
+ %4004 = "mhlo.broadcast_in_dim"(%1360) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4005 = mhlo.multiply %4003, %4004 : tensor<1x384x128xf32>
+ %4006 = "mhlo.broadcast_in_dim"(%1359) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4007 = mhlo.add %4005, %4006 : tensor<1x384x128xf32>
+ %4008 = "mhlo.reshape"(%4007) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4009 = "mhlo.dot"(%4008, %1378) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4010 = "mhlo.broadcast_in_dim"(%1377) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4011 = mhlo.add %4009, %4010 : tensor<384x512xf32>
+ %4012 = "mhlo.reshape"(%4011) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4013 = mhlo.maximum %4012, %1119 : tensor<1x384x512xf32>
+ %4014 = "mhlo.reshape"(%4013) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4015 = "mhlo.dot"(%4014, %1382) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4016 = "mhlo.broadcast_in_dim"(%1381) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4017 = mhlo.add %4015, %4016 : tensor<384x128xf32>
+ %4018 = "mhlo.reshape"(%4017) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4019 = mhlo.add %4018, %4007 : tensor<1x384x128xf32>
+ %4020 = "mhlo.broadcast_in_dim"(%1380) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4021 = mhlo.multiply %4019, %4020 : tensor<1x384x128xf32>
+ %4022 = "mhlo.broadcast_in_dim"(%1379) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4023 = mhlo.add %4021, %4022 : tensor<1x384x128xf32>
+ %4024 = "mhlo.reshape"(%4023) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4025 = "mhlo.dot"(%4024, %1384) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4026 = "mhlo.broadcast_in_dim"(%1383) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4027 = mhlo.add %4025, %4026 : tensor<384x512xf32>
+ %4028 = "mhlo.reshape"(%4027) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4029 = mhlo.maximum %4028, %1119 : tensor<1x384x512xf32>
+ %4030 = "mhlo.reshape"(%4029) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4031 = "mhlo.dot"(%4030, %1388) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4032 = "mhlo.broadcast_in_dim"(%1387) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4033 = mhlo.add %4031, %4032 : tensor<384x128xf32>
+ %4034 = "mhlo.reshape"(%4033) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4035 = mhlo.add %4034, %4023 : tensor<1x384x128xf32>
+ %4036 = "mhlo.broadcast_in_dim"(%1386) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4037 = mhlo.multiply %4035, %4036 : tensor<1x384x128xf32>
+ %4038 = "mhlo.broadcast_in_dim"(%1385) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4039 = mhlo.add %4037, %4038 : tensor<1x384x128xf32>
+ %4040 = "mhlo.reshape"(%4039) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4041 = "mhlo.dot"(%4040, %1390) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4042 = "mhlo.broadcast_in_dim"(%1389) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4043 = mhlo.add %4041, %4042 : tensor<384x512xf32>
+ %4044 = "mhlo.reshape"(%4043) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4045 = mhlo.maximum %4044, %1119 : tensor<1x384x512xf32>
+ %4046 = "mhlo.reshape"(%4045) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4047 = "mhlo.dot"(%4046, %1394) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4048 = "mhlo.broadcast_in_dim"(%1393) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4049 = mhlo.add %4047, %4048 : tensor<384x128xf32>
+ %4050 = "mhlo.reshape"(%4049) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4051 = mhlo.add %4050, %4039 : tensor<1x384x128xf32>
+ %4052 = "mhlo.broadcast_in_dim"(%1392) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4053 = mhlo.multiply %4051, %4052 : tensor<1x384x128xf32>
+ %4054 = "mhlo.broadcast_in_dim"(%1391) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4055 = mhlo.add %4053, %4054 : tensor<1x384x128xf32>
+ %4056 = "mhlo.reshape"(%4055) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4057 = "mhlo.dot"(%4056, %1396) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4058 = "mhlo.broadcast_in_dim"(%1395) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4059 = mhlo.add %4057, %4058 : tensor<384x512xf32>
+ %4060 = "mhlo.reshape"(%4059) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4061 = mhlo.maximum %4060, %1119 : tensor<1x384x512xf32>
+ %4062 = "mhlo.reshape"(%4061) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4063 = "mhlo.dot"(%4062, %1404) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4064 = "mhlo.broadcast_in_dim"(%1403) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4065 = mhlo.add %4063, %4064 : tensor<384x128xf32>
+ %4066 = "mhlo.reshape"(%4065) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4067 = mhlo.add %4066, %4055 : tensor<1x384x128xf32>
+ %4068 = "mhlo.broadcast_in_dim"(%1398) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4069 = mhlo.multiply %4067, %4068 : tensor<1x384x128xf32>
+ %4070 = "mhlo.broadcast_in_dim"(%1397) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4071 = mhlo.add %4069, %4070 : tensor<1x384x128xf32>
+ %4072 = "mhlo.reshape"(%4071) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4073 = "mhlo.dot"(%4072, %1402) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4074 = "mhlo.broadcast_in_dim"(%1401) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4075 = mhlo.add %4073, %4074 : tensor<384x512xf32>
+ %4076 = "mhlo.reshape"(%4075) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4077 = mhlo.add %4076, %3951 : tensor<1x384x512xf32>
+ %4078 = "mhlo.broadcast_in_dim"(%1400) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4079 = mhlo.multiply %4077, %4078 : tensor<1x384x512xf32>
+ %4080 = "mhlo.broadcast_in_dim"(%1399) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4081 = mhlo.add %4079, %4080 : tensor<1x384x512xf32>
+ %4082 = "mhlo.reshape"(%4081) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4083 = "mhlo.dot"(%4082, %1414) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4084 = "mhlo.broadcast_in_dim"(%1413) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4085 = mhlo.add %4083, %4084 : tensor<384x128xf32>
+ %4086 = "mhlo.reshape"(%4085) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4087 = "mhlo.transpose"(%4086) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4088 = "mhlo.dot"(%4082, %1418) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4089 = "mhlo.reshape"(%4088) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4090 = "mhlo.broadcast_in_dim"(%1417) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4091 = mhlo.add %4089, %4090 : tensor<1x384x128xf32>
+ %4092 = "mhlo.broadcast_in_dim"(%1416) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4093 = mhlo.multiply %4091, %4092 : tensor<1x384x128xf32>
+ %4094 = "mhlo.broadcast_in_dim"(%1415) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4095 = mhlo.add %4093, %4094 : tensor<1x384x128xf32>
+ %4096 = "mhlo.reshape"(%4095) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4097 = "mhlo.dot"(%4096, %1410) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4098 = "mhlo.broadcast_in_dim"(%1409) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4099 = mhlo.add %4097, %4098 : tensor<384x128xf32>
+ %4100 = "mhlo.reshape"(%4099) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4101 = "mhlo.transpose"(%4100) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4102 = "mhlo.dot"(%4096, %1412) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4103 = "mhlo.broadcast_in_dim"(%1411) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4104 = mhlo.add %4102, %4103 : tensor<384x128xf32>
+ %4105 = "mhlo.reshape"(%4104) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4106 = "mhlo.transpose"(%4105) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4107 = "mhlo.dot_general"(%4106, %4101) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4108 = mhlo.multiply %4107, %1114 : tensor<1x4x384x384xf32>
+ %4109 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4110 = mhlo.add %4108, %4109 : tensor<1x4x384x384xf32>
+ %4111 = "mhlo.reduce"(%4110, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4112 = "mhlo.broadcast_in_dim"(%4111) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4113 = mhlo.subtract %4110, %4112 : tensor<1x4x384x384xf32>
+ %4114 = "mhlo.exponential"(%4113) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4115 = "mhlo.reduce"(%4114, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4116 = "mhlo.broadcast_in_dim"(%4115) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4117 = mhlo.divide %4114, %4116 : tensor<1x4x384x384xf32>
+ %4118 = "mhlo.dot_general"(%4117, %4087) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4119 = "mhlo.transpose"(%4118) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4120 = "mhlo.reshape"(%4119) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4121 = "mhlo.dot"(%4120, %1408) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4122 = "mhlo.broadcast_in_dim"(%1407) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4123 = mhlo.add %4121, %4122 : tensor<384x128xf32>
+ %4124 = "mhlo.reshape"(%4123) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4125 = "mhlo.dot"(%4082, %1422) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4126 = "mhlo.broadcast_in_dim"(%1421) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4127 = mhlo.add %4125, %4126 : tensor<384x128xf32>
+ %4128 = "mhlo.reshape"(%4127) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4129 = "mhlo.broadcast_in_dim"(%1420) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4130 = mhlo.multiply %4128, %4129 : tensor<1x384x128xf32>
+ %4131 = "mhlo.broadcast_in_dim"(%1419) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4132 = mhlo.add %4130, %4131 : tensor<1x384x128xf32>
+ %4133 = mhlo.add %4124, %4132 : tensor<1x384x128xf32>
+ %4134 = "mhlo.broadcast_in_dim"(%1406) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4135 = mhlo.multiply %4133, %4134 : tensor<1x384x128xf32>
+ %4136 = "mhlo.broadcast_in_dim"(%1405) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4137 = mhlo.add %4135, %4136 : tensor<1x384x128xf32>
+ %4138 = "mhlo.reshape"(%4137) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4139 = "mhlo.dot"(%4138, %1424) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4140 = "mhlo.broadcast_in_dim"(%1423) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4141 = mhlo.add %4139, %4140 : tensor<384x512xf32>
+ %4142 = "mhlo.reshape"(%4141) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4143 = mhlo.maximum %4142, %1119 : tensor<1x384x512xf32>
+ %4144 = "mhlo.reshape"(%4143) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4145 = "mhlo.dot"(%4144, %1428) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4146 = "mhlo.broadcast_in_dim"(%1427) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4147 = mhlo.add %4145, %4146 : tensor<384x128xf32>
+ %4148 = "mhlo.reshape"(%4147) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4149 = mhlo.add %4148, %4137 : tensor<1x384x128xf32>
+ %4150 = "mhlo.broadcast_in_dim"(%1426) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4151 = mhlo.multiply %4149, %4150 : tensor<1x384x128xf32>
+ %4152 = "mhlo.broadcast_in_dim"(%1425) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4153 = mhlo.add %4151, %4152 : tensor<1x384x128xf32>
+ %4154 = "mhlo.reshape"(%4153) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4155 = "mhlo.dot"(%4154, %1430) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4156 = "mhlo.broadcast_in_dim"(%1429) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4157 = mhlo.add %4155, %4156 : tensor<384x512xf32>
+ %4158 = "mhlo.reshape"(%4157) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4159 = mhlo.maximum %4158, %1119 : tensor<1x384x512xf32>
+ %4160 = "mhlo.reshape"(%4159) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4161 = "mhlo.dot"(%4160, %1434) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4162 = "mhlo.broadcast_in_dim"(%1433) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4163 = mhlo.add %4161, %4162 : tensor<384x128xf32>
+ %4164 = "mhlo.reshape"(%4163) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4165 = mhlo.add %4164, %4153 : tensor<1x384x128xf32>
+ %4166 = "mhlo.broadcast_in_dim"(%1432) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4167 = mhlo.multiply %4165, %4166 : tensor<1x384x128xf32>
+ %4168 = "mhlo.broadcast_in_dim"(%1431) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4169 = mhlo.add %4167, %4168 : tensor<1x384x128xf32>
+ %4170 = "mhlo.reshape"(%4169) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4171 = "mhlo.dot"(%4170, %1436) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4172 = "mhlo.broadcast_in_dim"(%1435) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4173 = mhlo.add %4171, %4172 : tensor<384x512xf32>
+ %4174 = "mhlo.reshape"(%4173) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4175 = mhlo.maximum %4174, %1119 : tensor<1x384x512xf32>
+ %4176 = "mhlo.reshape"(%4175) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4177 = "mhlo.dot"(%4176, %1440) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4178 = "mhlo.broadcast_in_dim"(%1439) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4179 = mhlo.add %4177, %4178 : tensor<384x128xf32>
+ %4180 = "mhlo.reshape"(%4179) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4181 = mhlo.add %4180, %4169 : tensor<1x384x128xf32>
+ %4182 = "mhlo.broadcast_in_dim"(%1438) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4183 = mhlo.multiply %4181, %4182 : tensor<1x384x128xf32>
+ %4184 = "mhlo.broadcast_in_dim"(%1437) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4185 = mhlo.add %4183, %4184 : tensor<1x384x128xf32>
+ %4186 = "mhlo.reshape"(%4185) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4187 = "mhlo.dot"(%4186, %1442) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4188 = "mhlo.broadcast_in_dim"(%1441) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4189 = mhlo.add %4187, %4188 : tensor<384x512xf32>
+ %4190 = "mhlo.reshape"(%4189) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4191 = mhlo.maximum %4190, %1119 : tensor<1x384x512xf32>
+ %4192 = "mhlo.reshape"(%4191) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4193 = "mhlo.dot"(%4192, %1450) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4194 = "mhlo.broadcast_in_dim"(%1449) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4195 = mhlo.add %4193, %4194 : tensor<384x128xf32>
+ %4196 = "mhlo.reshape"(%4195) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4197 = mhlo.add %4196, %4185 : tensor<1x384x128xf32>
+ %4198 = "mhlo.broadcast_in_dim"(%1444) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4199 = mhlo.multiply %4197, %4198 : tensor<1x384x128xf32>
+ %4200 = "mhlo.broadcast_in_dim"(%1443) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4201 = mhlo.add %4199, %4200 : tensor<1x384x128xf32>
+ %4202 = "mhlo.reshape"(%4201) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4203 = "mhlo.dot"(%4202, %1448) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4204 = "mhlo.broadcast_in_dim"(%1447) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4205 = mhlo.add %4203, %4204 : tensor<384x512xf32>
+ %4206 = "mhlo.reshape"(%4205) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4207 = mhlo.add %4206, %4081 : tensor<1x384x512xf32>
+ %4208 = "mhlo.broadcast_in_dim"(%1446) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4209 = mhlo.multiply %4207, %4208 : tensor<1x384x512xf32>
+ %4210 = "mhlo.broadcast_in_dim"(%1445) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4211 = mhlo.add %4209, %4210 : tensor<1x384x512xf32>
+ %4212 = "mhlo.reshape"(%4211) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4213 = "mhlo.dot"(%4212, %1460) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4214 = "mhlo.broadcast_in_dim"(%1459) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4215 = mhlo.add %4213, %4214 : tensor<384x128xf32>
+ %4216 = "mhlo.reshape"(%4215) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4217 = "mhlo.transpose"(%4216) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4218 = "mhlo.dot"(%4212, %1464) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4219 = "mhlo.reshape"(%4218) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4220 = "mhlo.broadcast_in_dim"(%1463) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4221 = mhlo.add %4219, %4220 : tensor<1x384x128xf32>
+ %4222 = "mhlo.broadcast_in_dim"(%1462) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4223 = mhlo.multiply %4221, %4222 : tensor<1x384x128xf32>
+ %4224 = "mhlo.broadcast_in_dim"(%1461) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4225 = mhlo.add %4223, %4224 : tensor<1x384x128xf32>
+ %4226 = "mhlo.reshape"(%4225) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4227 = "mhlo.dot"(%4226, %1456) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4228 = "mhlo.broadcast_in_dim"(%1455) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4229 = mhlo.add %4227, %4228 : tensor<384x128xf32>
+ %4230 = "mhlo.reshape"(%4229) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4231 = "mhlo.transpose"(%4230) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4232 = "mhlo.dot"(%4226, %1458) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4233 = "mhlo.broadcast_in_dim"(%1457) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4234 = mhlo.add %4232, %4233 : tensor<384x128xf32>
+ %4235 = "mhlo.reshape"(%4234) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4236 = "mhlo.transpose"(%4235) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4237 = "mhlo.dot_general"(%4236, %4231) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4238 = mhlo.multiply %4237, %1114 : tensor<1x4x384x384xf32>
+ %4239 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4240 = mhlo.add %4238, %4239 : tensor<1x4x384x384xf32>
+ %4241 = "mhlo.reduce"(%4240, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4242 = "mhlo.broadcast_in_dim"(%4241) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4243 = mhlo.subtract %4240, %4242 : tensor<1x4x384x384xf32>
+ %4244 = "mhlo.exponential"(%4243) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4245 = "mhlo.reduce"(%4244, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4246 = "mhlo.broadcast_in_dim"(%4245) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4247 = mhlo.divide %4244, %4246 : tensor<1x4x384x384xf32>
+ %4248 = "mhlo.dot_general"(%4247, %4217) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4249 = "mhlo.transpose"(%4248) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4250 = "mhlo.reshape"(%4249) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4251 = "mhlo.dot"(%4250, %1454) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4252 = "mhlo.broadcast_in_dim"(%1453) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4253 = mhlo.add %4251, %4252 : tensor<384x128xf32>
+ %4254 = "mhlo.reshape"(%4253) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4255 = "mhlo.dot"(%4212, %1468) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4256 = "mhlo.broadcast_in_dim"(%1467) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4257 = mhlo.add %4255, %4256 : tensor<384x128xf32>
+ %4258 = "mhlo.reshape"(%4257) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4259 = "mhlo.broadcast_in_dim"(%1466) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4260 = mhlo.multiply %4258, %4259 : tensor<1x384x128xf32>
+ %4261 = "mhlo.broadcast_in_dim"(%1465) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4262 = mhlo.add %4260, %4261 : tensor<1x384x128xf32>
+ %4263 = mhlo.add %4254, %4262 : tensor<1x384x128xf32>
+ %4264 = "mhlo.broadcast_in_dim"(%1452) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4265 = mhlo.multiply %4263, %4264 : tensor<1x384x128xf32>
+ %4266 = "mhlo.broadcast_in_dim"(%1451) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4267 = mhlo.add %4265, %4266 : tensor<1x384x128xf32>
+ %4268 = "mhlo.reshape"(%4267) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4269 = "mhlo.dot"(%4268, %1470) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4270 = "mhlo.broadcast_in_dim"(%1469) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4271 = mhlo.add %4269, %4270 : tensor<384x512xf32>
+ %4272 = "mhlo.reshape"(%4271) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4273 = mhlo.maximum %4272, %1119 : tensor<1x384x512xf32>
+ %4274 = "mhlo.reshape"(%4273) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4275 = "mhlo.dot"(%4274, %1474) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4276 = "mhlo.broadcast_in_dim"(%1473) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4277 = mhlo.add %4275, %4276 : tensor<384x128xf32>
+ %4278 = "mhlo.reshape"(%4277) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4279 = mhlo.add %4278, %4267 : tensor<1x384x128xf32>
+ %4280 = "mhlo.broadcast_in_dim"(%1472) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4281 = mhlo.multiply %4279, %4280 : tensor<1x384x128xf32>
+ %4282 = "mhlo.broadcast_in_dim"(%1471) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4283 = mhlo.add %4281, %4282 : tensor<1x384x128xf32>
+ %4284 = "mhlo.reshape"(%4283) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4285 = "mhlo.dot"(%4284, %1476) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4286 = "mhlo.broadcast_in_dim"(%1475) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4287 = mhlo.add %4285, %4286 : tensor<384x512xf32>
+ %4288 = "mhlo.reshape"(%4287) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4289 = mhlo.maximum %4288, %1119 : tensor<1x384x512xf32>
+ %4290 = "mhlo.reshape"(%4289) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4291 = "mhlo.dot"(%4290, %1480) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4292 = "mhlo.broadcast_in_dim"(%1479) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4293 = mhlo.add %4291, %4292 : tensor<384x128xf32>
+ %4294 = "mhlo.reshape"(%4293) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4295 = mhlo.add %4294, %4283 : tensor<1x384x128xf32>
+ %4296 = "mhlo.broadcast_in_dim"(%1478) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4297 = mhlo.multiply %4295, %4296 : tensor<1x384x128xf32>
+ %4298 = "mhlo.broadcast_in_dim"(%1477) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4299 = mhlo.add %4297, %4298 : tensor<1x384x128xf32>
+ %4300 = "mhlo.reshape"(%4299) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4301 = "mhlo.dot"(%4300, %1482) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4302 = "mhlo.broadcast_in_dim"(%1481) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4303 = mhlo.add %4301, %4302 : tensor<384x512xf32>
+ %4304 = "mhlo.reshape"(%4303) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4305 = mhlo.maximum %4304, %1119 : tensor<1x384x512xf32>
+ %4306 = "mhlo.reshape"(%4305) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4307 = "mhlo.dot"(%4306, %1486) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4308 = "mhlo.broadcast_in_dim"(%1485) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4309 = mhlo.add %4307, %4308 : tensor<384x128xf32>
+ %4310 = "mhlo.reshape"(%4309) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4311 = mhlo.add %4310, %4299 : tensor<1x384x128xf32>
+ %4312 = "mhlo.broadcast_in_dim"(%1484) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4313 = mhlo.multiply %4311, %4312 : tensor<1x384x128xf32>
+ %4314 = "mhlo.broadcast_in_dim"(%1483) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4315 = mhlo.add %4313, %4314 : tensor<1x384x128xf32>
+ %4316 = "mhlo.reshape"(%4315) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4317 = "mhlo.dot"(%4316, %1488) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4318 = "mhlo.broadcast_in_dim"(%1487) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4319 = mhlo.add %4317, %4318 : tensor<384x512xf32>
+ %4320 = "mhlo.reshape"(%4319) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4321 = mhlo.maximum %4320, %1119 : tensor<1x384x512xf32>
+ %4322 = "mhlo.reshape"(%4321) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4323 = "mhlo.dot"(%4322, %1496) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4324 = "mhlo.broadcast_in_dim"(%1495) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4325 = mhlo.add %4323, %4324 : tensor<384x128xf32>
+ %4326 = "mhlo.reshape"(%4325) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4327 = mhlo.add %4326, %4315 : tensor<1x384x128xf32>
+ %4328 = "mhlo.broadcast_in_dim"(%1490) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4329 = mhlo.multiply %4327, %4328 : tensor<1x384x128xf32>
+ %4330 = "mhlo.broadcast_in_dim"(%1489) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4331 = mhlo.add %4329, %4330 : tensor<1x384x128xf32>
+ %4332 = "mhlo.reshape"(%4331) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4333 = "mhlo.dot"(%4332, %1494) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4334 = "mhlo.broadcast_in_dim"(%1493) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4335 = mhlo.add %4333, %4334 : tensor<384x512xf32>
+ %4336 = "mhlo.reshape"(%4335) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4337 = mhlo.add %4336, %4211 : tensor<1x384x512xf32>
+ %4338 = "mhlo.broadcast_in_dim"(%1492) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4339 = mhlo.multiply %4337, %4338 : tensor<1x384x512xf32>
+ %4340 = "mhlo.broadcast_in_dim"(%1491) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4341 = mhlo.add %4339, %4340 : tensor<1x384x512xf32>
+ %4342 = "mhlo.reshape"(%4341) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4343 = "mhlo.dot"(%4342, %1506) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4344 = "mhlo.broadcast_in_dim"(%1505) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4345 = mhlo.add %4343, %4344 : tensor<384x128xf32>
+ %4346 = "mhlo.reshape"(%4345) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4347 = "mhlo.transpose"(%4346) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4348 = "mhlo.dot"(%4342, %1510) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4349 = "mhlo.reshape"(%4348) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4350 = "mhlo.broadcast_in_dim"(%1509) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4351 = mhlo.add %4349, %4350 : tensor<1x384x128xf32>
+ %4352 = "mhlo.broadcast_in_dim"(%1508) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4353 = mhlo.multiply %4351, %4352 : tensor<1x384x128xf32>
+ %4354 = "mhlo.broadcast_in_dim"(%1507) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4355 = mhlo.add %4353, %4354 : tensor<1x384x128xf32>
+ %4356 = "mhlo.reshape"(%4355) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4357 = "mhlo.dot"(%4356, %1502) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4358 = "mhlo.broadcast_in_dim"(%1501) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4359 = mhlo.add %4357, %4358 : tensor<384x128xf32>
+ %4360 = "mhlo.reshape"(%4359) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4361 = "mhlo.transpose"(%4360) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4362 = "mhlo.dot"(%4356, %1504) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4363 = "mhlo.broadcast_in_dim"(%1503) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4364 = mhlo.add %4362, %4363 : tensor<384x128xf32>
+ %4365 = "mhlo.reshape"(%4364) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4366 = "mhlo.transpose"(%4365) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4367 = "mhlo.dot_general"(%4366, %4361) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4368 = mhlo.multiply %4367, %1114 : tensor<1x4x384x384xf32>
+ %4369 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4370 = mhlo.add %4368, %4369 : tensor<1x4x384x384xf32>
+ %4371 = "mhlo.reduce"(%4370, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4372 = "mhlo.broadcast_in_dim"(%4371) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4373 = mhlo.subtract %4370, %4372 : tensor<1x4x384x384xf32>
+ %4374 = "mhlo.exponential"(%4373) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4375 = "mhlo.reduce"(%4374, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4376 = "mhlo.broadcast_in_dim"(%4375) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4377 = mhlo.divide %4374, %4376 : tensor<1x4x384x384xf32>
+ %4378 = "mhlo.dot_general"(%4377, %4347) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4379 = "mhlo.transpose"(%4378) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4380 = "mhlo.reshape"(%4379) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4381 = "mhlo.dot"(%4380, %1500) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4382 = "mhlo.broadcast_in_dim"(%1499) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4383 = mhlo.add %4381, %4382 : tensor<384x128xf32>
+ %4384 = "mhlo.reshape"(%4383) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4385 = "mhlo.dot"(%4342, %1514) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4386 = "mhlo.broadcast_in_dim"(%1513) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4387 = mhlo.add %4385, %4386 : tensor<384x128xf32>
+ %4388 = "mhlo.reshape"(%4387) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4389 = "mhlo.broadcast_in_dim"(%1512) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4390 = mhlo.multiply %4388, %4389 : tensor<1x384x128xf32>
+ %4391 = "mhlo.broadcast_in_dim"(%1511) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4392 = mhlo.add %4390, %4391 : tensor<1x384x128xf32>
+ %4393 = mhlo.add %4384, %4392 : tensor<1x384x128xf32>
+ %4394 = "mhlo.broadcast_in_dim"(%1498) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4395 = mhlo.multiply %4393, %4394 : tensor<1x384x128xf32>
+ %4396 = "mhlo.broadcast_in_dim"(%1497) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4397 = mhlo.add %4395, %4396 : tensor<1x384x128xf32>
+ %4398 = "mhlo.reshape"(%4397) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4399 = "mhlo.dot"(%4398, %1516) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4400 = "mhlo.broadcast_in_dim"(%1515) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4401 = mhlo.add %4399, %4400 : tensor<384x512xf32>
+ %4402 = "mhlo.reshape"(%4401) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4403 = mhlo.maximum %4402, %1119 : tensor<1x384x512xf32>
+ %4404 = "mhlo.reshape"(%4403) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4405 = "mhlo.dot"(%4404, %1520) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4406 = "mhlo.broadcast_in_dim"(%1519) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4407 = mhlo.add %4405, %4406 : tensor<384x128xf32>
+ %4408 = "mhlo.reshape"(%4407) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4409 = mhlo.add %4408, %4397 : tensor<1x384x128xf32>
+ %4410 = "mhlo.broadcast_in_dim"(%1518) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4411 = mhlo.multiply %4409, %4410 : tensor<1x384x128xf32>
+ %4412 = "mhlo.broadcast_in_dim"(%1517) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4413 = mhlo.add %4411, %4412 : tensor<1x384x128xf32>
+ %4414 = "mhlo.reshape"(%4413) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4415 = "mhlo.dot"(%4414, %1522) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4416 = "mhlo.broadcast_in_dim"(%1521) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4417 = mhlo.add %4415, %4416 : tensor<384x512xf32>
+ %4418 = "mhlo.reshape"(%4417) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4419 = mhlo.maximum %4418, %1119 : tensor<1x384x512xf32>
+ %4420 = "mhlo.reshape"(%4419) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4421 = "mhlo.dot"(%4420, %1526) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4422 = "mhlo.broadcast_in_dim"(%1525) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4423 = mhlo.add %4421, %4422 : tensor<384x128xf32>
+ %4424 = "mhlo.reshape"(%4423) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4425 = mhlo.add %4424, %4413 : tensor<1x384x128xf32>
+ %4426 = "mhlo.broadcast_in_dim"(%1524) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4427 = mhlo.multiply %4425, %4426 : tensor<1x384x128xf32>
+ %4428 = "mhlo.broadcast_in_dim"(%1523) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4429 = mhlo.add %4427, %4428 : tensor<1x384x128xf32>
+ %4430 = "mhlo.reshape"(%4429) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4431 = "mhlo.dot"(%4430, %1528) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4432 = "mhlo.broadcast_in_dim"(%1527) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4433 = mhlo.add %4431, %4432 : tensor<384x512xf32>
+ %4434 = "mhlo.reshape"(%4433) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4435 = mhlo.maximum %4434, %1119 : tensor<1x384x512xf32>
+ %4436 = "mhlo.reshape"(%4435) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4437 = "mhlo.dot"(%4436, %1532) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4438 = "mhlo.broadcast_in_dim"(%1531) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4439 = mhlo.add %4437, %4438 : tensor<384x128xf32>
+ %4440 = "mhlo.reshape"(%4439) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4441 = mhlo.add %4440, %4429 : tensor<1x384x128xf32>
+ %4442 = "mhlo.broadcast_in_dim"(%1530) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4443 = mhlo.multiply %4441, %4442 : tensor<1x384x128xf32>
+ %4444 = "mhlo.broadcast_in_dim"(%1529) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4445 = mhlo.add %4443, %4444 : tensor<1x384x128xf32>
+ %4446 = "mhlo.reshape"(%4445) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4447 = "mhlo.dot"(%4446, %1534) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4448 = "mhlo.broadcast_in_dim"(%1533) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4449 = mhlo.add %4447, %4448 : tensor<384x512xf32>
+ %4450 = "mhlo.reshape"(%4449) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4451 = mhlo.maximum %4450, %1119 : tensor<1x384x512xf32>
+ %4452 = "mhlo.reshape"(%4451) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4453 = "mhlo.dot"(%4452, %1542) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4454 = "mhlo.broadcast_in_dim"(%1541) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4455 = mhlo.add %4453, %4454 : tensor<384x128xf32>
+ %4456 = "mhlo.reshape"(%4455) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4457 = mhlo.add %4456, %4445 : tensor<1x384x128xf32>
+ %4458 = "mhlo.broadcast_in_dim"(%1536) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4459 = mhlo.multiply %4457, %4458 : tensor<1x384x128xf32>
+ %4460 = "mhlo.broadcast_in_dim"(%1535) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4461 = mhlo.add %4459, %4460 : tensor<1x384x128xf32>
+ %4462 = "mhlo.reshape"(%4461) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4463 = "mhlo.dot"(%4462, %1540) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4464 = "mhlo.broadcast_in_dim"(%1539) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4465 = mhlo.add %4463, %4464 : tensor<384x512xf32>
+ %4466 = "mhlo.reshape"(%4465) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4467 = mhlo.add %4466, %4341 : tensor<1x384x512xf32>
+ %4468 = "mhlo.broadcast_in_dim"(%1538) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4469 = mhlo.multiply %4467, %4468 : tensor<1x384x512xf32>
+ %4470 = "mhlo.broadcast_in_dim"(%1537) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4471 = mhlo.add %4469, %4470 : tensor<1x384x512xf32>
+ %4472 = "mhlo.reshape"(%4471) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4473 = "mhlo.dot"(%4472, %1552) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4474 = "mhlo.broadcast_in_dim"(%1551) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4475 = mhlo.add %4473, %4474 : tensor<384x128xf32>
+ %4476 = "mhlo.reshape"(%4475) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4477 = "mhlo.transpose"(%4476) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4478 = "mhlo.dot"(%4472, %1556) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4479 = "mhlo.reshape"(%4478) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4480 = "mhlo.broadcast_in_dim"(%1555) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4481 = mhlo.add %4479, %4480 : tensor<1x384x128xf32>
+ %4482 = "mhlo.broadcast_in_dim"(%1554) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4483 = mhlo.multiply %4481, %4482 : tensor<1x384x128xf32>
+ %4484 = "mhlo.broadcast_in_dim"(%1553) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4485 = mhlo.add %4483, %4484 : tensor<1x384x128xf32>
+ %4486 = "mhlo.reshape"(%4485) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4487 = "mhlo.dot"(%4486, %1548) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4488 = "mhlo.broadcast_in_dim"(%1547) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4489 = mhlo.add %4487, %4488 : tensor<384x128xf32>
+ %4490 = "mhlo.reshape"(%4489) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4491 = "mhlo.transpose"(%4490) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4492 = "mhlo.dot"(%4486, %1550) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4493 = "mhlo.broadcast_in_dim"(%1549) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4494 = mhlo.add %4492, %4493 : tensor<384x128xf32>
+ %4495 = "mhlo.reshape"(%4494) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4496 = "mhlo.transpose"(%4495) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4497 = "mhlo.dot_general"(%4496, %4491) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4498 = mhlo.multiply %4497, %1114 : tensor<1x4x384x384xf32>
+ %4499 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4500 = mhlo.add %4498, %4499 : tensor<1x4x384x384xf32>
+ %4501 = "mhlo.reduce"(%4500, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4502 = "mhlo.broadcast_in_dim"(%4501) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4503 = mhlo.subtract %4500, %4502 : tensor<1x4x384x384xf32>
+ %4504 = "mhlo.exponential"(%4503) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4505 = "mhlo.reduce"(%4504, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4506 = "mhlo.broadcast_in_dim"(%4505) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4507 = mhlo.divide %4504, %4506 : tensor<1x4x384x384xf32>
+ %4508 = "mhlo.dot_general"(%4507, %4477) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4509 = "mhlo.transpose"(%4508) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4510 = "mhlo.reshape"(%4509) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4511 = "mhlo.dot"(%4510, %1546) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4512 = "mhlo.broadcast_in_dim"(%1545) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4513 = mhlo.add %4511, %4512 : tensor<384x128xf32>
+ %4514 = "mhlo.reshape"(%4513) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4515 = "mhlo.dot"(%4472, %1560) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4516 = "mhlo.broadcast_in_dim"(%1559) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4517 = mhlo.add %4515, %4516 : tensor<384x128xf32>
+ %4518 = "mhlo.reshape"(%4517) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4519 = "mhlo.broadcast_in_dim"(%1558) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4520 = mhlo.multiply %4518, %4519 : tensor<1x384x128xf32>
+ %4521 = "mhlo.broadcast_in_dim"(%1557) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4522 = mhlo.add %4520, %4521 : tensor<1x384x128xf32>
+ %4523 = mhlo.add %4514, %4522 : tensor<1x384x128xf32>
+ %4524 = "mhlo.broadcast_in_dim"(%1544) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4525 = mhlo.multiply %4523, %4524 : tensor<1x384x128xf32>
+ %4526 = "mhlo.broadcast_in_dim"(%1543) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4527 = mhlo.add %4525, %4526 : tensor<1x384x128xf32>
+ %4528 = "mhlo.reshape"(%4527) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4529 = "mhlo.dot"(%4528, %1562) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4530 = "mhlo.broadcast_in_dim"(%1561) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4531 = mhlo.add %4529, %4530 : tensor<384x512xf32>
+ %4532 = "mhlo.reshape"(%4531) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4533 = mhlo.maximum %4532, %1119 : tensor<1x384x512xf32>
+ %4534 = "mhlo.reshape"(%4533) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4535 = "mhlo.dot"(%4534, %1566) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4536 = "mhlo.broadcast_in_dim"(%1565) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4537 = mhlo.add %4535, %4536 : tensor<384x128xf32>
+ %4538 = "mhlo.reshape"(%4537) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4539 = mhlo.add %4538, %4527 : tensor<1x384x128xf32>
+ %4540 = "mhlo.broadcast_in_dim"(%1564) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4541 = mhlo.multiply %4539, %4540 : tensor<1x384x128xf32>
+ %4542 = "mhlo.broadcast_in_dim"(%1563) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4543 = mhlo.add %4541, %4542 : tensor<1x384x128xf32>
+ %4544 = "mhlo.reshape"(%4543) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4545 = "mhlo.dot"(%4544, %1568) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4546 = "mhlo.broadcast_in_dim"(%1567) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4547 = mhlo.add %4545, %4546 : tensor<384x512xf32>
+ %4548 = "mhlo.reshape"(%4547) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4549 = mhlo.maximum %4548, %1119 : tensor<1x384x512xf32>
+ %4550 = "mhlo.reshape"(%4549) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4551 = "mhlo.dot"(%4550, %1572) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4552 = "mhlo.broadcast_in_dim"(%1571) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4553 = mhlo.add %4551, %4552 : tensor<384x128xf32>
+ %4554 = "mhlo.reshape"(%4553) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4555 = mhlo.add %4554, %4543 : tensor<1x384x128xf32>
+ %4556 = "mhlo.broadcast_in_dim"(%1570) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4557 = mhlo.multiply %4555, %4556 : tensor<1x384x128xf32>
+ %4558 = "mhlo.broadcast_in_dim"(%1569) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4559 = mhlo.add %4557, %4558 : tensor<1x384x128xf32>
+ %4560 = "mhlo.reshape"(%4559) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4561 = "mhlo.dot"(%4560, %1574) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4562 = "mhlo.broadcast_in_dim"(%1573) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4563 = mhlo.add %4561, %4562 : tensor<384x512xf32>
+ %4564 = "mhlo.reshape"(%4563) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4565 = mhlo.maximum %4564, %1119 : tensor<1x384x512xf32>
+ %4566 = "mhlo.reshape"(%4565) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4567 = "mhlo.dot"(%4566, %1578) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4568 = "mhlo.broadcast_in_dim"(%1577) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4569 = mhlo.add %4567, %4568 : tensor<384x128xf32>
+ %4570 = "mhlo.reshape"(%4569) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4571 = mhlo.add %4570, %4559 : tensor<1x384x128xf32>
+ %4572 = "mhlo.broadcast_in_dim"(%1576) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4573 = mhlo.multiply %4571, %4572 : tensor<1x384x128xf32>
+ %4574 = "mhlo.broadcast_in_dim"(%1575) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4575 = mhlo.add %4573, %4574 : tensor<1x384x128xf32>
+ %4576 = "mhlo.reshape"(%4575) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4577 = "mhlo.dot"(%4576, %1580) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4578 = "mhlo.broadcast_in_dim"(%1579) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4579 = mhlo.add %4577, %4578 : tensor<384x512xf32>
+ %4580 = "mhlo.reshape"(%4579) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4581 = mhlo.maximum %4580, %1119 : tensor<1x384x512xf32>
+ %4582 = "mhlo.reshape"(%4581) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4583 = "mhlo.dot"(%4582, %1588) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4584 = "mhlo.broadcast_in_dim"(%1587) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4585 = mhlo.add %4583, %4584 : tensor<384x128xf32>
+ %4586 = "mhlo.reshape"(%4585) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4587 = mhlo.add %4586, %4575 : tensor<1x384x128xf32>
+ %4588 = "mhlo.broadcast_in_dim"(%1582) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4589 = mhlo.multiply %4587, %4588 : tensor<1x384x128xf32>
+ %4590 = "mhlo.broadcast_in_dim"(%1581) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4591 = mhlo.add %4589, %4590 : tensor<1x384x128xf32>
+ %4592 = "mhlo.reshape"(%4591) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4593 = "mhlo.dot"(%4592, %1586) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4594 = "mhlo.broadcast_in_dim"(%1585) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4595 = mhlo.add %4593, %4594 : tensor<384x512xf32>
+ %4596 = "mhlo.reshape"(%4595) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4597 = mhlo.add %4596, %4471 : tensor<1x384x512xf32>
+ %4598 = "mhlo.broadcast_in_dim"(%1584) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4599 = mhlo.multiply %4597, %4598 : tensor<1x384x512xf32>
+ %4600 = "mhlo.broadcast_in_dim"(%1583) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4601 = mhlo.add %4599, %4600 : tensor<1x384x512xf32>
+ %4602 = "mhlo.reshape"(%4601) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4603 = "mhlo.dot"(%4602, %1598) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4604 = "mhlo.broadcast_in_dim"(%1597) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4605 = mhlo.add %4603, %4604 : tensor<384x128xf32>
+ %4606 = "mhlo.reshape"(%4605) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4607 = "mhlo.transpose"(%4606) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4608 = "mhlo.dot"(%4602, %1602) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4609 = "mhlo.reshape"(%4608) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4610 = "mhlo.broadcast_in_dim"(%1601) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4611 = mhlo.add %4609, %4610 : tensor<1x384x128xf32>
+ %4612 = "mhlo.broadcast_in_dim"(%1600) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4613 = mhlo.multiply %4611, %4612 : tensor<1x384x128xf32>
+ %4614 = "mhlo.broadcast_in_dim"(%1599) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4615 = mhlo.add %4613, %4614 : tensor<1x384x128xf32>
+ %4616 = "mhlo.reshape"(%4615) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4617 = "mhlo.dot"(%4616, %1594) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4618 = "mhlo.broadcast_in_dim"(%1593) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4619 = mhlo.add %4617, %4618 : tensor<384x128xf32>
+ %4620 = "mhlo.reshape"(%4619) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4621 = "mhlo.transpose"(%4620) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4622 = "mhlo.dot"(%4616, %1596) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4623 = "mhlo.broadcast_in_dim"(%1595) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4624 = mhlo.add %4622, %4623 : tensor<384x128xf32>
+ %4625 = "mhlo.reshape"(%4624) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4626 = "mhlo.transpose"(%4625) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4627 = "mhlo.dot_general"(%4626, %4621) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4628 = mhlo.multiply %4627, %1114 : tensor<1x4x384x384xf32>
+ %4629 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4630 = mhlo.add %4628, %4629 : tensor<1x4x384x384xf32>
+ %4631 = "mhlo.reduce"(%4630, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4632 = "mhlo.broadcast_in_dim"(%4631) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4633 = mhlo.subtract %4630, %4632 : tensor<1x4x384x384xf32>
+ %4634 = "mhlo.exponential"(%4633) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4635 = "mhlo.reduce"(%4634, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4636 = "mhlo.broadcast_in_dim"(%4635) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4637 = mhlo.divide %4634, %4636 : tensor<1x4x384x384xf32>
+ %4638 = "mhlo.dot_general"(%4637, %4607) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4639 = "mhlo.transpose"(%4638) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4640 = "mhlo.reshape"(%4639) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4641 = "mhlo.dot"(%4640, %1592) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4642 = "mhlo.broadcast_in_dim"(%1591) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4643 = mhlo.add %4641, %4642 : tensor<384x128xf32>
+ %4644 = "mhlo.reshape"(%4643) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4645 = "mhlo.dot"(%4602, %1606) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4646 = "mhlo.broadcast_in_dim"(%1605) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4647 = mhlo.add %4645, %4646 : tensor<384x128xf32>
+ %4648 = "mhlo.reshape"(%4647) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4649 = "mhlo.broadcast_in_dim"(%1604) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4650 = mhlo.multiply %4648, %4649 : tensor<1x384x128xf32>
+ %4651 = "mhlo.broadcast_in_dim"(%1603) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4652 = mhlo.add %4650, %4651 : tensor<1x384x128xf32>
+ %4653 = mhlo.add %4644, %4652 : tensor<1x384x128xf32>
+ %4654 = "mhlo.broadcast_in_dim"(%1590) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4655 = mhlo.multiply %4653, %4654 : tensor<1x384x128xf32>
+ %4656 = "mhlo.broadcast_in_dim"(%1589) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4657 = mhlo.add %4655, %4656 : tensor<1x384x128xf32>
+ %4658 = "mhlo.reshape"(%4657) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4659 = "mhlo.dot"(%4658, %1608) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4660 = "mhlo.broadcast_in_dim"(%1607) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4661 = mhlo.add %4659, %4660 : tensor<384x512xf32>
+ %4662 = "mhlo.reshape"(%4661) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4663 = mhlo.maximum %4662, %1119 : tensor<1x384x512xf32>
+ %4664 = "mhlo.reshape"(%4663) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4665 = "mhlo.dot"(%4664, %1612) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4666 = "mhlo.broadcast_in_dim"(%1611) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4667 = mhlo.add %4665, %4666 : tensor<384x128xf32>
+ %4668 = "mhlo.reshape"(%4667) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4669 = mhlo.add %4668, %4657 : tensor<1x384x128xf32>
+ %4670 = "mhlo.broadcast_in_dim"(%1610) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4671 = mhlo.multiply %4669, %4670 : tensor<1x384x128xf32>
+ %4672 = "mhlo.broadcast_in_dim"(%1609) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4673 = mhlo.add %4671, %4672 : tensor<1x384x128xf32>
+ %4674 = "mhlo.reshape"(%4673) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4675 = "mhlo.dot"(%4674, %1614) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4676 = "mhlo.broadcast_in_dim"(%1613) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4677 = mhlo.add %4675, %4676 : tensor<384x512xf32>
+ %4678 = "mhlo.reshape"(%4677) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4679 = mhlo.maximum %4678, %1119 : tensor<1x384x512xf32>
+ %4680 = "mhlo.reshape"(%4679) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4681 = "mhlo.dot"(%4680, %1618) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4682 = "mhlo.broadcast_in_dim"(%1617) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4683 = mhlo.add %4681, %4682 : tensor<384x128xf32>
+ %4684 = "mhlo.reshape"(%4683) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4685 = mhlo.add %4684, %4673 : tensor<1x384x128xf32>
+ %4686 = "mhlo.broadcast_in_dim"(%1616) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4687 = mhlo.multiply %4685, %4686 : tensor<1x384x128xf32>
+ %4688 = "mhlo.broadcast_in_dim"(%1615) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4689 = mhlo.add %4687, %4688 : tensor<1x384x128xf32>
+ %4690 = "mhlo.reshape"(%4689) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4691 = "mhlo.dot"(%4690, %1620) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4692 = "mhlo.broadcast_in_dim"(%1619) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4693 = mhlo.add %4691, %4692 : tensor<384x512xf32>
+ %4694 = "mhlo.reshape"(%4693) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4695 = mhlo.maximum %4694, %1119 : tensor<1x384x512xf32>
+ %4696 = "mhlo.reshape"(%4695) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4697 = "mhlo.dot"(%4696, %1624) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4698 = "mhlo.broadcast_in_dim"(%1623) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4699 = mhlo.add %4697, %4698 : tensor<384x128xf32>
+ %4700 = "mhlo.reshape"(%4699) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4701 = mhlo.add %4700, %4689 : tensor<1x384x128xf32>
+ %4702 = "mhlo.broadcast_in_dim"(%1622) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4703 = mhlo.multiply %4701, %4702 : tensor<1x384x128xf32>
+ %4704 = "mhlo.broadcast_in_dim"(%1621) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4705 = mhlo.add %4703, %4704 : tensor<1x384x128xf32>
+ %4706 = "mhlo.reshape"(%4705) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4707 = "mhlo.dot"(%4706, %1626) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4708 = "mhlo.broadcast_in_dim"(%1625) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4709 = mhlo.add %4707, %4708 : tensor<384x512xf32>
+ %4710 = "mhlo.reshape"(%4709) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4711 = mhlo.maximum %4710, %1119 : tensor<1x384x512xf32>
+ %4712 = "mhlo.reshape"(%4711) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4713 = "mhlo.dot"(%4712, %1634) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4714 = "mhlo.broadcast_in_dim"(%1633) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4715 = mhlo.add %4713, %4714 : tensor<384x128xf32>
+ %4716 = "mhlo.reshape"(%4715) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4717 = mhlo.add %4716, %4705 : tensor<1x384x128xf32>
+ %4718 = "mhlo.broadcast_in_dim"(%1628) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4719 = mhlo.multiply %4717, %4718 : tensor<1x384x128xf32>
+ %4720 = "mhlo.broadcast_in_dim"(%1627) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4721 = mhlo.add %4719, %4720 : tensor<1x384x128xf32>
+ %4722 = "mhlo.reshape"(%4721) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4723 = "mhlo.dot"(%4722, %1632) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4724 = "mhlo.broadcast_in_dim"(%1631) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4725 = mhlo.add %4723, %4724 : tensor<384x512xf32>
+ %4726 = "mhlo.reshape"(%4725) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4727 = mhlo.add %4726, %4601 : tensor<1x384x512xf32>
+ %4728 = "mhlo.broadcast_in_dim"(%1630) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4729 = mhlo.multiply %4727, %4728 : tensor<1x384x512xf32>
+ %4730 = "mhlo.broadcast_in_dim"(%1629) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4731 = mhlo.add %4729, %4730 : tensor<1x384x512xf32>
+ %4732 = "mhlo.reshape"(%4731) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4733 = "mhlo.dot"(%4732, %1644) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4734 = "mhlo.broadcast_in_dim"(%1643) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4735 = mhlo.add %4733, %4734 : tensor<384x128xf32>
+ %4736 = "mhlo.reshape"(%4735) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4737 = "mhlo.transpose"(%4736) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4738 = "mhlo.dot"(%4732, %1648) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4739 = "mhlo.reshape"(%4738) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4740 = "mhlo.broadcast_in_dim"(%1647) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4741 = mhlo.add %4739, %4740 : tensor<1x384x128xf32>
+ %4742 = "mhlo.broadcast_in_dim"(%1646) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4743 = mhlo.multiply %4741, %4742 : tensor<1x384x128xf32>
+ %4744 = "mhlo.broadcast_in_dim"(%1645) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4745 = mhlo.add %4743, %4744 : tensor<1x384x128xf32>
+ %4746 = "mhlo.reshape"(%4745) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4747 = "mhlo.dot"(%4746, %1640) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4748 = "mhlo.broadcast_in_dim"(%1639) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4749 = mhlo.add %4747, %4748 : tensor<384x128xf32>
+ %4750 = "mhlo.reshape"(%4749) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4751 = "mhlo.transpose"(%4750) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4752 = "mhlo.dot"(%4746, %1642) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4753 = "mhlo.broadcast_in_dim"(%1641) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4754 = mhlo.add %4752, %4753 : tensor<384x128xf32>
+ %4755 = "mhlo.reshape"(%4754) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4756 = "mhlo.transpose"(%4755) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4757 = "mhlo.dot_general"(%4756, %4751) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4758 = mhlo.multiply %4757, %1114 : tensor<1x4x384x384xf32>
+ %4759 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4760 = mhlo.add %4758, %4759 : tensor<1x4x384x384xf32>
+ %4761 = "mhlo.reduce"(%4760, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4762 = "mhlo.broadcast_in_dim"(%4761) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4763 = mhlo.subtract %4760, %4762 : tensor<1x4x384x384xf32>
+ %4764 = "mhlo.exponential"(%4763) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4765 = "mhlo.reduce"(%4764, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4766 = "mhlo.broadcast_in_dim"(%4765) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4767 = mhlo.divide %4764, %4766 : tensor<1x4x384x384xf32>
+ %4768 = "mhlo.dot_general"(%4767, %4737) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4769 = "mhlo.transpose"(%4768) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4770 = "mhlo.reshape"(%4769) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4771 = "mhlo.dot"(%4770, %1638) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4772 = "mhlo.broadcast_in_dim"(%1637) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4773 = mhlo.add %4771, %4772 : tensor<384x128xf32>
+ %4774 = "mhlo.reshape"(%4773) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4775 = "mhlo.dot"(%4732, %1652) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4776 = "mhlo.broadcast_in_dim"(%1651) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4777 = mhlo.add %4775, %4776 : tensor<384x128xf32>
+ %4778 = "mhlo.reshape"(%4777) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4779 = "mhlo.broadcast_in_dim"(%1650) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4780 = mhlo.multiply %4778, %4779 : tensor<1x384x128xf32>
+ %4781 = "mhlo.broadcast_in_dim"(%1649) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4782 = mhlo.add %4780, %4781 : tensor<1x384x128xf32>
+ %4783 = mhlo.add %4774, %4782 : tensor<1x384x128xf32>
+ %4784 = "mhlo.broadcast_in_dim"(%1636) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4785 = mhlo.multiply %4783, %4784 : tensor<1x384x128xf32>
+ %4786 = "mhlo.broadcast_in_dim"(%1635) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4787 = mhlo.add %4785, %4786 : tensor<1x384x128xf32>
+ %4788 = "mhlo.reshape"(%4787) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4789 = "mhlo.dot"(%4788, %1654) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4790 = "mhlo.broadcast_in_dim"(%1653) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4791 = mhlo.add %4789, %4790 : tensor<384x512xf32>
+ %4792 = "mhlo.reshape"(%4791) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4793 = mhlo.maximum %4792, %1119 : tensor<1x384x512xf32>
+ %4794 = "mhlo.reshape"(%4793) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4795 = "mhlo.dot"(%4794, %1658) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4796 = "mhlo.broadcast_in_dim"(%1657) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4797 = mhlo.add %4795, %4796 : tensor<384x128xf32>
+ %4798 = "mhlo.reshape"(%4797) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4799 = mhlo.add %4798, %4787 : tensor<1x384x128xf32>
+ %4800 = "mhlo.broadcast_in_dim"(%1656) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4801 = mhlo.multiply %4799, %4800 : tensor<1x384x128xf32>
+ %4802 = "mhlo.broadcast_in_dim"(%1655) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4803 = mhlo.add %4801, %4802 : tensor<1x384x128xf32>
+ %4804 = "mhlo.reshape"(%4803) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4805 = "mhlo.dot"(%4804, %1660) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4806 = "mhlo.broadcast_in_dim"(%1659) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4807 = mhlo.add %4805, %4806 : tensor<384x512xf32>
+ %4808 = "mhlo.reshape"(%4807) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4809 = mhlo.maximum %4808, %1119 : tensor<1x384x512xf32>
+ %4810 = "mhlo.reshape"(%4809) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4811 = "mhlo.dot"(%4810, %1664) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4812 = "mhlo.broadcast_in_dim"(%1663) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4813 = mhlo.add %4811, %4812 : tensor<384x128xf32>
+ %4814 = "mhlo.reshape"(%4813) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4815 = mhlo.add %4814, %4803 : tensor<1x384x128xf32>
+ %4816 = "mhlo.broadcast_in_dim"(%1662) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4817 = mhlo.multiply %4815, %4816 : tensor<1x384x128xf32>
+ %4818 = "mhlo.broadcast_in_dim"(%1661) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4819 = mhlo.add %4817, %4818 : tensor<1x384x128xf32>
+ %4820 = "mhlo.reshape"(%4819) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4821 = "mhlo.dot"(%4820, %1666) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4822 = "mhlo.broadcast_in_dim"(%1665) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4823 = mhlo.add %4821, %4822 : tensor<384x512xf32>
+ %4824 = "mhlo.reshape"(%4823) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4825 = mhlo.maximum %4824, %1119 : tensor<1x384x512xf32>
+ %4826 = "mhlo.reshape"(%4825) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4827 = "mhlo.dot"(%4826, %1670) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4828 = "mhlo.broadcast_in_dim"(%1669) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4829 = mhlo.add %4827, %4828 : tensor<384x128xf32>
+ %4830 = "mhlo.reshape"(%4829) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4831 = mhlo.add %4830, %4819 : tensor<1x384x128xf32>
+ %4832 = "mhlo.broadcast_in_dim"(%1668) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4833 = mhlo.multiply %4831, %4832 : tensor<1x384x128xf32>
+ %4834 = "mhlo.broadcast_in_dim"(%1667) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4835 = mhlo.add %4833, %4834 : tensor<1x384x128xf32>
+ %4836 = "mhlo.reshape"(%4835) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4837 = "mhlo.dot"(%4836, %1672) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4838 = "mhlo.broadcast_in_dim"(%1671) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4839 = mhlo.add %4837, %4838 : tensor<384x512xf32>
+ %4840 = "mhlo.reshape"(%4839) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4841 = mhlo.maximum %4840, %1119 : tensor<1x384x512xf32>
+ %4842 = "mhlo.reshape"(%4841) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4843 = "mhlo.dot"(%4842, %1680) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4844 = "mhlo.broadcast_in_dim"(%1679) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4845 = mhlo.add %4843, %4844 : tensor<384x128xf32>
+ %4846 = "mhlo.reshape"(%4845) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4847 = mhlo.add %4846, %4835 : tensor<1x384x128xf32>
+ %4848 = "mhlo.broadcast_in_dim"(%1674) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4849 = mhlo.multiply %4847, %4848 : tensor<1x384x128xf32>
+ %4850 = "mhlo.broadcast_in_dim"(%1673) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4851 = mhlo.add %4849, %4850 : tensor<1x384x128xf32>
+ %4852 = "mhlo.reshape"(%4851) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4853 = "mhlo.dot"(%4852, %1678) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4854 = "mhlo.broadcast_in_dim"(%1677) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4855 = mhlo.add %4853, %4854 : tensor<384x512xf32>
+ %4856 = "mhlo.reshape"(%4855) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4857 = mhlo.add %4856, %4731 : tensor<1x384x512xf32>
+ %4858 = "mhlo.broadcast_in_dim"(%1676) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4859 = mhlo.multiply %4857, %4858 : tensor<1x384x512xf32>
+ %4860 = "mhlo.broadcast_in_dim"(%1675) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4861 = mhlo.add %4859, %4860 : tensor<1x384x512xf32>
+ %4862 = "mhlo.reshape"(%4861) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4863 = "mhlo.dot"(%4862, %1736) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4864 = "mhlo.broadcast_in_dim"(%1735) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4865 = mhlo.add %4863, %4864 : tensor<384x128xf32>
+ %4866 = "mhlo.reshape"(%4865) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4867 = "mhlo.transpose"(%4866) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4868 = "mhlo.dot"(%4862, %1740) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4869 = "mhlo.reshape"(%4868) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4870 = "mhlo.broadcast_in_dim"(%1739) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4871 = mhlo.add %4869, %4870 : tensor<1x384x128xf32>
+ %4872 = "mhlo.broadcast_in_dim"(%1738) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4873 = mhlo.multiply %4871, %4872 : tensor<1x384x128xf32>
+ %4874 = "mhlo.broadcast_in_dim"(%1737) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4875 = mhlo.add %4873, %4874 : tensor<1x384x128xf32>
+ %4876 = "mhlo.reshape"(%4875) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4877 = "mhlo.dot"(%4876, %1732) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4878 = "mhlo.broadcast_in_dim"(%1731) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4879 = mhlo.add %4877, %4878 : tensor<384x128xf32>
+ %4880 = "mhlo.reshape"(%4879) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4881 = "mhlo.transpose"(%4880) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4882 = "mhlo.dot"(%4876, %1734) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4883 = "mhlo.broadcast_in_dim"(%1733) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4884 = mhlo.add %4882, %4883 : tensor<384x128xf32>
+ %4885 = "mhlo.reshape"(%4884) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4886 = "mhlo.transpose"(%4885) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4887 = "mhlo.dot_general"(%4886, %4881) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %4888 = mhlo.multiply %4887, %1114 : tensor<1x4x384x384xf32>
+ %4889 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4890 = mhlo.add %4888, %4889 : tensor<1x4x384x384xf32>
+ %4891 = "mhlo.reduce"(%4890, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4892 = "mhlo.broadcast_in_dim"(%4891) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4893 = mhlo.subtract %4890, %4892 : tensor<1x4x384x384xf32>
+ %4894 = "mhlo.exponential"(%4893) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %4895 = "mhlo.reduce"(%4894, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %4896 = "mhlo.broadcast_in_dim"(%4895) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %4897 = mhlo.divide %4894, %4896 : tensor<1x4x384x384xf32>
+ %4898 = "mhlo.dot_general"(%4897, %4867) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %4899 = "mhlo.transpose"(%4898) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %4900 = "mhlo.reshape"(%4899) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %4901 = "mhlo.dot"(%4900, %1730) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %4902 = "mhlo.broadcast_in_dim"(%1729) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4903 = mhlo.add %4901, %4902 : tensor<384x128xf32>
+ %4904 = "mhlo.reshape"(%4903) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4905 = "mhlo.dot"(%4862, %1744) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4906 = "mhlo.broadcast_in_dim"(%1743) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4907 = mhlo.add %4905, %4906 : tensor<384x128xf32>
+ %4908 = "mhlo.reshape"(%4907) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4909 = "mhlo.broadcast_in_dim"(%1742) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4910 = mhlo.multiply %4908, %4909 : tensor<1x384x128xf32>
+ %4911 = "mhlo.broadcast_in_dim"(%1741) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4912 = mhlo.add %4910, %4911 : tensor<1x384x128xf32>
+ %4913 = mhlo.add %4904, %4912 : tensor<1x384x128xf32>
+ %4914 = "mhlo.broadcast_in_dim"(%1728) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4915 = mhlo.multiply %4913, %4914 : tensor<1x384x128xf32>
+ %4916 = "mhlo.broadcast_in_dim"(%1727) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4917 = mhlo.add %4915, %4916 : tensor<1x384x128xf32>
+ %4918 = "mhlo.reshape"(%4917) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4919 = "mhlo.dot"(%4918, %1746) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4920 = "mhlo.broadcast_in_dim"(%1745) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4921 = mhlo.add %4919, %4920 : tensor<384x512xf32>
+ %4922 = "mhlo.reshape"(%4921) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4923 = mhlo.maximum %4922, %1119 : tensor<1x384x512xf32>
+ %4924 = "mhlo.reshape"(%4923) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4925 = "mhlo.dot"(%4924, %1750) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4926 = "mhlo.broadcast_in_dim"(%1749) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4927 = mhlo.add %4925, %4926 : tensor<384x128xf32>
+ %4928 = "mhlo.reshape"(%4927) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4929 = mhlo.add %4928, %4917 : tensor<1x384x128xf32>
+ %4930 = "mhlo.broadcast_in_dim"(%1748) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4931 = mhlo.multiply %4929, %4930 : tensor<1x384x128xf32>
+ %4932 = "mhlo.broadcast_in_dim"(%1747) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4933 = mhlo.add %4931, %4932 : tensor<1x384x128xf32>
+ %4934 = "mhlo.reshape"(%4933) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4935 = "mhlo.dot"(%4934, %1752) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4936 = "mhlo.broadcast_in_dim"(%1751) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4937 = mhlo.add %4935, %4936 : tensor<384x512xf32>
+ %4938 = "mhlo.reshape"(%4937) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4939 = mhlo.maximum %4938, %1119 : tensor<1x384x512xf32>
+ %4940 = "mhlo.reshape"(%4939) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4941 = "mhlo.dot"(%4940, %1756) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4942 = "mhlo.broadcast_in_dim"(%1755) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4943 = mhlo.add %4941, %4942 : tensor<384x128xf32>
+ %4944 = "mhlo.reshape"(%4943) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4945 = mhlo.add %4944, %4933 : tensor<1x384x128xf32>
+ %4946 = "mhlo.broadcast_in_dim"(%1754) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4947 = mhlo.multiply %4945, %4946 : tensor<1x384x128xf32>
+ %4948 = "mhlo.broadcast_in_dim"(%1753) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4949 = mhlo.add %4947, %4948 : tensor<1x384x128xf32>
+ %4950 = "mhlo.reshape"(%4949) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4951 = "mhlo.dot"(%4950, %1758) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4952 = "mhlo.broadcast_in_dim"(%1757) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4953 = mhlo.add %4951, %4952 : tensor<384x512xf32>
+ %4954 = "mhlo.reshape"(%4953) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4955 = mhlo.maximum %4954, %1119 : tensor<1x384x512xf32>
+ %4956 = "mhlo.reshape"(%4955) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4957 = "mhlo.dot"(%4956, %1762) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4958 = "mhlo.broadcast_in_dim"(%1761) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4959 = mhlo.add %4957, %4958 : tensor<384x128xf32>
+ %4960 = "mhlo.reshape"(%4959) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4961 = mhlo.add %4960, %4949 : tensor<1x384x128xf32>
+ %4962 = "mhlo.broadcast_in_dim"(%1760) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4963 = mhlo.multiply %4961, %4962 : tensor<1x384x128xf32>
+ %4964 = "mhlo.broadcast_in_dim"(%1759) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4965 = mhlo.add %4963, %4964 : tensor<1x384x128xf32>
+ %4966 = "mhlo.reshape"(%4965) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4967 = "mhlo.dot"(%4966, %1764) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4968 = "mhlo.broadcast_in_dim"(%1763) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4969 = mhlo.add %4967, %4968 : tensor<384x512xf32>
+ %4970 = "mhlo.reshape"(%4969) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4971 = mhlo.maximum %4970, %1119 : tensor<1x384x512xf32>
+ %4972 = "mhlo.reshape"(%4971) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4973 = "mhlo.dot"(%4972, %1772) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4974 = "mhlo.broadcast_in_dim"(%1771) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4975 = mhlo.add %4973, %4974 : tensor<384x128xf32>
+ %4976 = "mhlo.reshape"(%4975) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %4977 = mhlo.add %4976, %4965 : tensor<1x384x128xf32>
+ %4978 = "mhlo.broadcast_in_dim"(%1766) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4979 = mhlo.multiply %4977, %4978 : tensor<1x384x128xf32>
+ %4980 = "mhlo.broadcast_in_dim"(%1765) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %4981 = mhlo.add %4979, %4980 : tensor<1x384x128xf32>
+ %4982 = "mhlo.reshape"(%4981) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %4983 = "mhlo.dot"(%4982, %1770) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %4984 = "mhlo.broadcast_in_dim"(%1769) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %4985 = mhlo.add %4983, %4984 : tensor<384x512xf32>
+ %4986 = "mhlo.reshape"(%4985) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %4987 = mhlo.add %4986, %4861 : tensor<1x384x512xf32>
+ %4988 = "mhlo.broadcast_in_dim"(%1768) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4989 = mhlo.multiply %4987, %4988 : tensor<1x384x512xf32>
+ %4990 = "mhlo.broadcast_in_dim"(%1767) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %4991 = mhlo.add %4989, %4990 : tensor<1x384x512xf32>
+ %4992 = "mhlo.reshape"(%4991) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %4993 = "mhlo.dot"(%4992, %1782) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4994 = "mhlo.broadcast_in_dim"(%1781) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %4995 = mhlo.add %4993, %4994 : tensor<384x128xf32>
+ %4996 = "mhlo.reshape"(%4995) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %4997 = "mhlo.transpose"(%4996) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %4998 = "mhlo.dot"(%4992, %1786) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %4999 = "mhlo.reshape"(%4998) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5000 = "mhlo.broadcast_in_dim"(%1785) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5001 = mhlo.add %4999, %5000 : tensor<1x384x128xf32>
+ %5002 = "mhlo.broadcast_in_dim"(%1784) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5003 = mhlo.multiply %5001, %5002 : tensor<1x384x128xf32>
+ %5004 = "mhlo.broadcast_in_dim"(%1783) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5005 = mhlo.add %5003, %5004 : tensor<1x384x128xf32>
+ %5006 = "mhlo.reshape"(%5005) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5007 = "mhlo.dot"(%5006, %1778) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5008 = "mhlo.broadcast_in_dim"(%1777) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5009 = mhlo.add %5007, %5008 : tensor<384x128xf32>
+ %5010 = "mhlo.reshape"(%5009) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5011 = "mhlo.transpose"(%5010) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5012 = "mhlo.dot"(%5006, %1780) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5013 = "mhlo.broadcast_in_dim"(%1779) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5014 = mhlo.add %5012, %5013 : tensor<384x128xf32>
+ %5015 = "mhlo.reshape"(%5014) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5016 = "mhlo.transpose"(%5015) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5017 = "mhlo.dot_general"(%5016, %5011) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %5018 = mhlo.multiply %5017, %1114 : tensor<1x4x384x384xf32>
+ %5019 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5020 = mhlo.add %5018, %5019 : tensor<1x4x384x384xf32>
+ %5021 = "mhlo.reduce"(%5020, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5022 = "mhlo.broadcast_in_dim"(%5021) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5023 = mhlo.subtract %5020, %5022 : tensor<1x4x384x384xf32>
+ %5024 = "mhlo.exponential"(%5023) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5025 = "mhlo.reduce"(%5024, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5026 = "mhlo.broadcast_in_dim"(%5025) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5027 = mhlo.divide %5024, %5026 : tensor<1x4x384x384xf32>
+ %5028 = "mhlo.dot_general"(%5027, %4997) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %5029 = "mhlo.transpose"(%5028) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %5030 = "mhlo.reshape"(%5029) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %5031 = "mhlo.dot"(%5030, %1776) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5032 = "mhlo.broadcast_in_dim"(%1775) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5033 = mhlo.add %5031, %5032 : tensor<384x128xf32>
+ %5034 = "mhlo.reshape"(%5033) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5035 = "mhlo.dot"(%4992, %1790) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5036 = "mhlo.broadcast_in_dim"(%1789) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5037 = mhlo.add %5035, %5036 : tensor<384x128xf32>
+ %5038 = "mhlo.reshape"(%5037) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5039 = "mhlo.broadcast_in_dim"(%1788) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5040 = mhlo.multiply %5038, %5039 : tensor<1x384x128xf32>
+ %5041 = "mhlo.broadcast_in_dim"(%1787) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5042 = mhlo.add %5040, %5041 : tensor<1x384x128xf32>
+ %5043 = mhlo.add %5034, %5042 : tensor<1x384x128xf32>
+ %5044 = "mhlo.broadcast_in_dim"(%1774) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5045 = mhlo.multiply %5043, %5044 : tensor<1x384x128xf32>
+ %5046 = "mhlo.broadcast_in_dim"(%1773) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5047 = mhlo.add %5045, %5046 : tensor<1x384x128xf32>
+ %5048 = "mhlo.reshape"(%5047) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5049 = "mhlo.dot"(%5048, %1792) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5050 = "mhlo.broadcast_in_dim"(%1791) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5051 = mhlo.add %5049, %5050 : tensor<384x512xf32>
+ %5052 = "mhlo.reshape"(%5051) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5053 = mhlo.maximum %5052, %1119 : tensor<1x384x512xf32>
+ %5054 = "mhlo.reshape"(%5053) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5055 = "mhlo.dot"(%5054, %1796) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5056 = "mhlo.broadcast_in_dim"(%1795) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5057 = mhlo.add %5055, %5056 : tensor<384x128xf32>
+ %5058 = "mhlo.reshape"(%5057) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5059 = mhlo.add %5058, %5047 : tensor<1x384x128xf32>
+ %5060 = "mhlo.broadcast_in_dim"(%1794) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5061 = mhlo.multiply %5059, %5060 : tensor<1x384x128xf32>
+ %5062 = "mhlo.broadcast_in_dim"(%1793) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5063 = mhlo.add %5061, %5062 : tensor<1x384x128xf32>
+ %5064 = "mhlo.reshape"(%5063) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5065 = "mhlo.dot"(%5064, %1798) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5066 = "mhlo.broadcast_in_dim"(%1797) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5067 = mhlo.add %5065, %5066 : tensor<384x512xf32>
+ %5068 = "mhlo.reshape"(%5067) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5069 = mhlo.maximum %5068, %1119 : tensor<1x384x512xf32>
+ %5070 = "mhlo.reshape"(%5069) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5071 = "mhlo.dot"(%5070, %1802) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5072 = "mhlo.broadcast_in_dim"(%1801) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5073 = mhlo.add %5071, %5072 : tensor<384x128xf32>
+ %5074 = "mhlo.reshape"(%5073) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5075 = mhlo.add %5074, %5063 : tensor<1x384x128xf32>
+ %5076 = "mhlo.broadcast_in_dim"(%1800) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5077 = mhlo.multiply %5075, %5076 : tensor<1x384x128xf32>
+ %5078 = "mhlo.broadcast_in_dim"(%1799) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5079 = mhlo.add %5077, %5078 : tensor<1x384x128xf32>
+ %5080 = "mhlo.reshape"(%5079) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5081 = "mhlo.dot"(%5080, %1804) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5082 = "mhlo.broadcast_in_dim"(%1803) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5083 = mhlo.add %5081, %5082 : tensor<384x512xf32>
+ %5084 = "mhlo.reshape"(%5083) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5085 = mhlo.maximum %5084, %1119 : tensor<1x384x512xf32>
+ %5086 = "mhlo.reshape"(%5085) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5087 = "mhlo.dot"(%5086, %1808) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5088 = "mhlo.broadcast_in_dim"(%1807) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5089 = mhlo.add %5087, %5088 : tensor<384x128xf32>
+ %5090 = "mhlo.reshape"(%5089) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5091 = mhlo.add %5090, %5079 : tensor<1x384x128xf32>
+ %5092 = "mhlo.broadcast_in_dim"(%1806) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5093 = mhlo.multiply %5091, %5092 : tensor<1x384x128xf32>
+ %5094 = "mhlo.broadcast_in_dim"(%1805) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5095 = mhlo.add %5093, %5094 : tensor<1x384x128xf32>
+ %5096 = "mhlo.reshape"(%5095) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5097 = "mhlo.dot"(%5096, %1810) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5098 = "mhlo.broadcast_in_dim"(%1809) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5099 = mhlo.add %5097, %5098 : tensor<384x512xf32>
+ %5100 = "mhlo.reshape"(%5099) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5101 = mhlo.maximum %5100, %1119 : tensor<1x384x512xf32>
+ %5102 = "mhlo.reshape"(%5101) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5103 = "mhlo.dot"(%5102, %1818) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5104 = "mhlo.broadcast_in_dim"(%1817) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5105 = mhlo.add %5103, %5104 : tensor<384x128xf32>
+ %5106 = "mhlo.reshape"(%5105) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5107 = mhlo.add %5106, %5095 : tensor<1x384x128xf32>
+ %5108 = "mhlo.broadcast_in_dim"(%1812) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5109 = mhlo.multiply %5107, %5108 : tensor<1x384x128xf32>
+ %5110 = "mhlo.broadcast_in_dim"(%1811) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5111 = mhlo.add %5109, %5110 : tensor<1x384x128xf32>
+ %5112 = "mhlo.reshape"(%5111) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5113 = "mhlo.dot"(%5112, %1816) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5114 = "mhlo.broadcast_in_dim"(%1815) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5115 = mhlo.add %5113, %5114 : tensor<384x512xf32>
+ %5116 = "mhlo.reshape"(%5115) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5117 = mhlo.add %5116, %4991 : tensor<1x384x512xf32>
+ %5118 = "mhlo.broadcast_in_dim"(%1814) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5119 = mhlo.multiply %5117, %5118 : tensor<1x384x512xf32>
+ %5120 = "mhlo.broadcast_in_dim"(%1813) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5121 = mhlo.add %5119, %5120 : tensor<1x384x512xf32>
+ %5122 = "mhlo.reshape"(%5121) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5123 = "mhlo.dot"(%5122, %1828) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5124 = "mhlo.broadcast_in_dim"(%1827) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5125 = mhlo.add %5123, %5124 : tensor<384x128xf32>
+ %5126 = "mhlo.reshape"(%5125) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5127 = "mhlo.transpose"(%5126) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5128 = "mhlo.dot"(%5122, %1832) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5129 = "mhlo.reshape"(%5128) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5130 = "mhlo.broadcast_in_dim"(%1831) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5131 = mhlo.add %5129, %5130 : tensor<1x384x128xf32>
+ %5132 = "mhlo.broadcast_in_dim"(%1830) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5133 = mhlo.multiply %5131, %5132 : tensor<1x384x128xf32>
+ %5134 = "mhlo.broadcast_in_dim"(%1829) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5135 = mhlo.add %5133, %5134 : tensor<1x384x128xf32>
+ %5136 = "mhlo.reshape"(%5135) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5137 = "mhlo.dot"(%5136, %1824) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5138 = "mhlo.broadcast_in_dim"(%1823) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5139 = mhlo.add %5137, %5138 : tensor<384x128xf32>
+ %5140 = "mhlo.reshape"(%5139) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5141 = "mhlo.transpose"(%5140) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5142 = "mhlo.dot"(%5136, %1826) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5143 = "mhlo.broadcast_in_dim"(%1825) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5144 = mhlo.add %5142, %5143 : tensor<384x128xf32>
+ %5145 = "mhlo.reshape"(%5144) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5146 = "mhlo.transpose"(%5145) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5147 = "mhlo.dot_general"(%5146, %5141) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %5148 = mhlo.multiply %5147, %1114 : tensor<1x4x384x384xf32>
+ %5149 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5150 = mhlo.add %5148, %5149 : tensor<1x4x384x384xf32>
+ %5151 = "mhlo.reduce"(%5150, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5152 = "mhlo.broadcast_in_dim"(%5151) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5153 = mhlo.subtract %5150, %5152 : tensor<1x4x384x384xf32>
+ %5154 = "mhlo.exponential"(%5153) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5155 = "mhlo.reduce"(%5154, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5156 = "mhlo.broadcast_in_dim"(%5155) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5157 = mhlo.divide %5154, %5156 : tensor<1x4x384x384xf32>
+ %5158 = "mhlo.dot_general"(%5157, %5127) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %5159 = "mhlo.transpose"(%5158) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %5160 = "mhlo.reshape"(%5159) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %5161 = "mhlo.dot"(%5160, %1822) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5162 = "mhlo.broadcast_in_dim"(%1821) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5163 = mhlo.add %5161, %5162 : tensor<384x128xf32>
+ %5164 = "mhlo.reshape"(%5163) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5165 = "mhlo.dot"(%5122, %1836) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5166 = "mhlo.broadcast_in_dim"(%1835) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5167 = mhlo.add %5165, %5166 : tensor<384x128xf32>
+ %5168 = "mhlo.reshape"(%5167) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5169 = "mhlo.broadcast_in_dim"(%1834) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5170 = mhlo.multiply %5168, %5169 : tensor<1x384x128xf32>
+ %5171 = "mhlo.broadcast_in_dim"(%1833) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5172 = mhlo.add %5170, %5171 : tensor<1x384x128xf32>
+ %5173 = mhlo.add %5164, %5172 : tensor<1x384x128xf32>
+ %5174 = "mhlo.broadcast_in_dim"(%1820) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5175 = mhlo.multiply %5173, %5174 : tensor<1x384x128xf32>
+ %5176 = "mhlo.broadcast_in_dim"(%1819) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5177 = mhlo.add %5175, %5176 : tensor<1x384x128xf32>
+ %5178 = "mhlo.reshape"(%5177) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5179 = "mhlo.dot"(%5178, %1838) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5180 = "mhlo.broadcast_in_dim"(%1837) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5181 = mhlo.add %5179, %5180 : tensor<384x512xf32>
+ %5182 = "mhlo.reshape"(%5181) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5183 = mhlo.maximum %5182, %1119 : tensor<1x384x512xf32>
+ %5184 = "mhlo.reshape"(%5183) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5185 = "mhlo.dot"(%5184, %1842) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5186 = "mhlo.broadcast_in_dim"(%1841) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5187 = mhlo.add %5185, %5186 : tensor<384x128xf32>
+ %5188 = "mhlo.reshape"(%5187) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5189 = mhlo.add %5188, %5177 : tensor<1x384x128xf32>
+ %5190 = "mhlo.broadcast_in_dim"(%1840) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5191 = mhlo.multiply %5189, %5190 : tensor<1x384x128xf32>
+ %5192 = "mhlo.broadcast_in_dim"(%1839) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5193 = mhlo.add %5191, %5192 : tensor<1x384x128xf32>
+ %5194 = "mhlo.reshape"(%5193) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5195 = "mhlo.dot"(%5194, %1844) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5196 = "mhlo.broadcast_in_dim"(%1843) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5197 = mhlo.add %5195, %5196 : tensor<384x512xf32>
+ %5198 = "mhlo.reshape"(%5197) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5199 = mhlo.maximum %5198, %1119 : tensor<1x384x512xf32>
+ %5200 = "mhlo.reshape"(%5199) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5201 = "mhlo.dot"(%5200, %1848) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5202 = "mhlo.broadcast_in_dim"(%1847) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5203 = mhlo.add %5201, %5202 : tensor<384x128xf32>
+ %5204 = "mhlo.reshape"(%5203) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5205 = mhlo.add %5204, %5193 : tensor<1x384x128xf32>
+ %5206 = "mhlo.broadcast_in_dim"(%1846) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5207 = mhlo.multiply %5205, %5206 : tensor<1x384x128xf32>
+ %5208 = "mhlo.broadcast_in_dim"(%1845) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5209 = mhlo.add %5207, %5208 : tensor<1x384x128xf32>
+ %5210 = "mhlo.reshape"(%5209) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5211 = "mhlo.dot"(%5210, %1850) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5212 = "mhlo.broadcast_in_dim"(%1849) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5213 = mhlo.add %5211, %5212 : tensor<384x512xf32>
+ %5214 = "mhlo.reshape"(%5213) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5215 = mhlo.maximum %5214, %1119 : tensor<1x384x512xf32>
+ %5216 = "mhlo.reshape"(%5215) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5217 = "mhlo.dot"(%5216, %1854) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5218 = "mhlo.broadcast_in_dim"(%1853) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5219 = mhlo.add %5217, %5218 : tensor<384x128xf32>
+ %5220 = "mhlo.reshape"(%5219) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5221 = mhlo.add %5220, %5209 : tensor<1x384x128xf32>
+ %5222 = "mhlo.broadcast_in_dim"(%1852) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5223 = mhlo.multiply %5221, %5222 : tensor<1x384x128xf32>
+ %5224 = "mhlo.broadcast_in_dim"(%1851) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5225 = mhlo.add %5223, %5224 : tensor<1x384x128xf32>
+ %5226 = "mhlo.reshape"(%5225) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5227 = "mhlo.dot"(%5226, %1856) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5228 = "mhlo.broadcast_in_dim"(%1855) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5229 = mhlo.add %5227, %5228 : tensor<384x512xf32>
+ %5230 = "mhlo.reshape"(%5229) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5231 = mhlo.maximum %5230, %1119 : tensor<1x384x512xf32>
+ %5232 = "mhlo.reshape"(%5231) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5233 = "mhlo.dot"(%5232, %1864) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5234 = "mhlo.broadcast_in_dim"(%1863) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5235 = mhlo.add %5233, %5234 : tensor<384x128xf32>
+ %5236 = "mhlo.reshape"(%5235) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5237 = mhlo.add %5236, %5225 : tensor<1x384x128xf32>
+ %5238 = "mhlo.broadcast_in_dim"(%1858) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5239 = mhlo.multiply %5237, %5238 : tensor<1x384x128xf32>
+ %5240 = "mhlo.broadcast_in_dim"(%1857) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5241 = mhlo.add %5239, %5240 : tensor<1x384x128xf32>
+ %5242 = "mhlo.reshape"(%5241) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5243 = "mhlo.dot"(%5242, %1862) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5244 = "mhlo.broadcast_in_dim"(%1861) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5245 = mhlo.add %5243, %5244 : tensor<384x512xf32>
+ %5246 = "mhlo.reshape"(%5245) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5247 = mhlo.add %5246, %5121 : tensor<1x384x512xf32>
+ %5248 = "mhlo.broadcast_in_dim"(%1860) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5249 = mhlo.multiply %5247, %5248 : tensor<1x384x512xf32>
+ %5250 = "mhlo.broadcast_in_dim"(%1859) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5251 = mhlo.add %5249, %5250 : tensor<1x384x512xf32>
+ %5252 = "mhlo.reshape"(%5251) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5253 = "mhlo.dot"(%5252, %1874) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5254 = "mhlo.broadcast_in_dim"(%1873) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5255 = mhlo.add %5253, %5254 : tensor<384x128xf32>
+ %5256 = "mhlo.reshape"(%5255) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5257 = "mhlo.transpose"(%5256) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5258 = "mhlo.dot"(%5252, %1878) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5259 = "mhlo.reshape"(%5258) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5260 = "mhlo.broadcast_in_dim"(%1877) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5261 = mhlo.add %5259, %5260 : tensor<1x384x128xf32>
+ %5262 = "mhlo.broadcast_in_dim"(%1876) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5263 = mhlo.multiply %5261, %5262 : tensor<1x384x128xf32>
+ %5264 = "mhlo.broadcast_in_dim"(%1875) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5265 = mhlo.add %5263, %5264 : tensor<1x384x128xf32>
+ %5266 = "mhlo.reshape"(%5265) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5267 = "mhlo.dot"(%5266, %1870) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5268 = "mhlo.broadcast_in_dim"(%1869) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5269 = mhlo.add %5267, %5268 : tensor<384x128xf32>
+ %5270 = "mhlo.reshape"(%5269) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5271 = "mhlo.transpose"(%5270) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5272 = "mhlo.dot"(%5266, %1872) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5273 = "mhlo.broadcast_in_dim"(%1871) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5274 = mhlo.add %5272, %5273 : tensor<384x128xf32>
+ %5275 = "mhlo.reshape"(%5274) : (tensor<384x128xf32>) -> tensor<1x384x4x32xf32>
+ %5276 = "mhlo.transpose"(%5275) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x384x4x32xf32>) -> tensor<1x4x384x32xf32>
+ %5277 = "mhlo.dot_general"(%5276, %5271) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<3> : tensor<1xi64>}} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x384xf32>
+ %5278 = mhlo.multiply %5277, %1114 : tensor<1x4x384x384xf32>
+ %5279 = "mhlo.broadcast_in_dim"(%2254) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5280 = mhlo.add %5278, %5279 : tensor<1x4x384x384xf32>
+ %5281 = "mhlo.reduce"(%5280, %1117) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.maximum %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5282 = "mhlo.broadcast_in_dim"(%5281) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5283 = mhlo.subtract %5280, %5282 : tensor<1x4x384x384xf32>
+ %5284 = "mhlo.exponential"(%5283) : (tensor<1x4x384x384xf32>) -> tensor<1x4x384x384xf32>
+ %5285 = "mhlo.reduce"(%5284, %1118) ( {
+ ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>): // no predecessors
+ %5393 = mhlo.add %arg3, %arg4 : tensor<f32>
+ "mhlo.return"(%5393) : (tensor<f32>) -> ()
+ }) {dimensions = dense<3> : tensor<1xi64>} : (tensor<1x4x384x384xf32>, tensor<f32>) -> tensor<1x4x384xf32>
+ %5286 = "mhlo.broadcast_in_dim"(%5285) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<1x4x384xf32>) -> tensor<1x4x384x384xf32>
+ %5287 = mhlo.divide %5284, %5286 : tensor<1x4x384x384xf32>
+ %5288 = "mhlo.dot_general"(%5287, %5257) {dot_dimension_numbers = {lhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, lhs_contracting_dimensions = dense<3> : tensor<1xi64>, rhs_batching_dimensions = dense<[0, 1]> : tensor<2xi64>, rhs_contracting_dimensions = dense<2> : tensor<1xi64>}} : (tensor<1x4x384x384xf32>, tensor<1x4x384x32xf32>) -> tensor<1x4x384x32xf32>
+ %5289 = "mhlo.transpose"(%5288) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x4x384x32xf32>) -> tensor<1x384x4x32xf32>
+ %5290 = "mhlo.reshape"(%5289) : (tensor<1x384x4x32xf32>) -> tensor<384x128xf32>
+ %5291 = "mhlo.dot"(%5290, %1868) : (tensor<384x128xf32>, tensor<128x128xf32>) -> tensor<384x128xf32>
+ %5292 = "mhlo.broadcast_in_dim"(%1867) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5293 = mhlo.add %5291, %5292 : tensor<384x128xf32>
+ %5294 = "mhlo.reshape"(%5293) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5295 = "mhlo.dot"(%5252, %1882) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5296 = "mhlo.broadcast_in_dim"(%1881) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5297 = mhlo.add %5295, %5296 : tensor<384x128xf32>
+ %5298 = "mhlo.reshape"(%5297) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5299 = "mhlo.broadcast_in_dim"(%1880) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5300 = mhlo.multiply %5298, %5299 : tensor<1x384x128xf32>
+ %5301 = "mhlo.broadcast_in_dim"(%1879) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5302 = mhlo.add %5300, %5301 : tensor<1x384x128xf32>
+ %5303 = mhlo.add %5294, %5302 : tensor<1x384x128xf32>
+ %5304 = "mhlo.broadcast_in_dim"(%1866) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5305 = mhlo.multiply %5303, %5304 : tensor<1x384x128xf32>
+ %5306 = "mhlo.broadcast_in_dim"(%1865) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5307 = mhlo.add %5305, %5306 : tensor<1x384x128xf32>
+ %5308 = "mhlo.reshape"(%5307) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5309 = "mhlo.dot"(%5308, %1884) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5310 = "mhlo.broadcast_in_dim"(%1883) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5311 = mhlo.add %5309, %5310 : tensor<384x512xf32>
+ %5312 = "mhlo.reshape"(%5311) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5313 = mhlo.maximum %5312, %1119 : tensor<1x384x512xf32>
+ %5314 = "mhlo.reshape"(%5313) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5315 = "mhlo.dot"(%5314, %1888) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5316 = "mhlo.broadcast_in_dim"(%1887) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5317 = mhlo.add %5315, %5316 : tensor<384x128xf32>
+ %5318 = "mhlo.reshape"(%5317) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5319 = mhlo.add %5318, %5307 : tensor<1x384x128xf32>
+ %5320 = "mhlo.broadcast_in_dim"(%1886) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5321 = mhlo.multiply %5319, %5320 : tensor<1x384x128xf32>
+ %5322 = "mhlo.broadcast_in_dim"(%1885) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5323 = mhlo.add %5321, %5322 : tensor<1x384x128xf32>
+ %5324 = "mhlo.reshape"(%5323) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5325 = "mhlo.dot"(%5324, %1890) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5326 = "mhlo.broadcast_in_dim"(%1889) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5327 = mhlo.add %5325, %5326 : tensor<384x512xf32>
+ %5328 = "mhlo.reshape"(%5327) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5329 = mhlo.maximum %5328, %1119 : tensor<1x384x512xf32>
+ %5330 = "mhlo.reshape"(%5329) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5331 = "mhlo.dot"(%5330, %1894) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5332 = "mhlo.broadcast_in_dim"(%1893) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5333 = mhlo.add %5331, %5332 : tensor<384x128xf32>
+ %5334 = "mhlo.reshape"(%5333) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5335 = mhlo.add %5334, %5323 : tensor<1x384x128xf32>
+ %5336 = "mhlo.broadcast_in_dim"(%1892) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5337 = mhlo.multiply %5335, %5336 : tensor<1x384x128xf32>
+ %5338 = "mhlo.broadcast_in_dim"(%1891) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5339 = mhlo.add %5337, %5338 : tensor<1x384x128xf32>
+ %5340 = "mhlo.reshape"(%5339) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5341 = "mhlo.dot"(%5340, %1896) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5342 = "mhlo.broadcast_in_dim"(%1895) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5343 = mhlo.add %5341, %5342 : tensor<384x512xf32>
+ %5344 = "mhlo.reshape"(%5343) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5345 = mhlo.maximum %5344, %1119 : tensor<1x384x512xf32>
+ %5346 = "mhlo.reshape"(%5345) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5347 = "mhlo.dot"(%5346, %1900) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5348 = "mhlo.broadcast_in_dim"(%1899) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5349 = mhlo.add %5347, %5348 : tensor<384x128xf32>
+ %5350 = "mhlo.reshape"(%5349) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5351 = mhlo.add %5350, %5339 : tensor<1x384x128xf32>
+ %5352 = "mhlo.broadcast_in_dim"(%1898) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5353 = mhlo.multiply %5351, %5352 : tensor<1x384x128xf32>
+ %5354 = "mhlo.broadcast_in_dim"(%1897) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5355 = mhlo.add %5353, %5354 : tensor<1x384x128xf32>
+ %5356 = "mhlo.reshape"(%5355) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5357 = "mhlo.dot"(%5356, %1902) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5358 = "mhlo.broadcast_in_dim"(%1901) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5359 = mhlo.add %5357, %5358 : tensor<384x512xf32>
+ %5360 = "mhlo.reshape"(%5359) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5361 = mhlo.maximum %5360, %1119 : tensor<1x384x512xf32>
+ %5362 = "mhlo.reshape"(%5361) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5363 = "mhlo.dot"(%5362, %1910) : (tensor<384x512xf32>, tensor<512x128xf32>) -> tensor<384x128xf32>
+ %5364 = "mhlo.broadcast_in_dim"(%1909) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<384x128xf32>
+ %5365 = mhlo.add %5363, %5364 : tensor<384x128xf32>
+ %5366 = "mhlo.reshape"(%5365) : (tensor<384x128xf32>) -> tensor<1x384x128xf32>
+ %5367 = mhlo.add %5366, %5355 : tensor<1x384x128xf32>
+ %5368 = "mhlo.broadcast_in_dim"(%1904) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5369 = mhlo.multiply %5367, %5368 : tensor<1x384x128xf32>
+ %5370 = "mhlo.broadcast_in_dim"(%1903) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x384x128xf32>
+ %5371 = mhlo.add %5369, %5370 : tensor<1x384x128xf32>
+ %5372 = "mhlo.reshape"(%5371) : (tensor<1x384x128xf32>) -> tensor<384x128xf32>
+ %5373 = "mhlo.dot"(%5372, %1908) : (tensor<384x128xf32>, tensor<128x512xf32>) -> tensor<384x512xf32>
+ %5374 = "mhlo.broadcast_in_dim"(%1907) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<384x512xf32>
+ %5375 = mhlo.add %5373, %5374 : tensor<384x512xf32>
+ %5376 = "mhlo.reshape"(%5375) : (tensor<384x512xf32>) -> tensor<1x384x512xf32>
+ %5377 = mhlo.add %5376, %5251 : tensor<1x384x512xf32>
+ %5378 = "mhlo.broadcast_in_dim"(%1906) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5379 = mhlo.multiply %5377, %5378 : tensor<1x384x512xf32>
+ %5380 = "mhlo.broadcast_in_dim"(%1905) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x384x512xf32>
+ %5381 = mhlo.add %5379, %5380 : tensor<1x384x512xf32>
+ %5382 = "mhlo.reshape"(%5381) : (tensor<1x384x512xf32>) -> tensor<384x512xf32>
+ %5383 = "mhlo.transpose"(%2234) {permutation = dense<[1, 0]> : tensor<2xi64>} : (tensor<2x512xf32>) -> tensor<512x2xf32>
+ %5384 = "mhlo.dot"(%5382, %5383) : (tensor<384x512xf32>, tensor<512x2xf32>) -> tensor<384x2xf32>
+ %5385 = "mhlo.broadcast_in_dim"(%2233) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<2xf32>) -> tensor<384x2xf32>
+ %5386 = mhlo.add %5384, %5385 : tensor<384x2xf32>
+ %5387 = "mhlo.reshape"(%5386) : (tensor<384x2xf32>) -> tensor<1x384x2xf32>
+ %5388 = "mhlo.transpose"(%5387) {permutation = dense<[2, 0, 1]> : tensor<3xi64>} : (tensor<1x384x2xf32>) -> tensor<2x1x384xf32>
+ %5389 = "mhlo.slice"(%5388) {limit_indices = dense<[1, 1, 384]> : tensor<3xi64>, start_indices = dense<0> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} : (tensor<2x1x384xf32>) -> tensor<1x1x384xf32>
+ %5390 = "mhlo.reshape"(%5389) : (tensor<1x1x384xf32>) -> tensor<1x384xf32>
+ %5391 = "mhlo.slice"(%5388) {limit_indices = dense<[2, 1, 384]> : tensor<3xi64>, start_indices = dense<[1, 0, 0]> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} : (tensor<2x1x384xf32>) -> tensor<1x1x384xf32>
+ %5392 = "mhlo.reshape"(%5391) : (tensor<1x1x384xf32>) -> tensor<1x384xf32>
+ check.expect_almost_eq_const(%5390, dense<895.256> : tensor<1x384xf32>) : tensor<1x384xf32>
+ check.expect_almost_eq_const(%5392, dense<895.256> : tensor<1x384xf32>) : tensor<1x384xf32>
+ return
+ }
+}
