| module { |
| util.global private @"__iree_flow___sm_node260__m.layer-2.kernel" {noinline} = dense<1.000000e+00> : tensor<3x3x3x16xf32> |
| util.global private @"__iree_flow___sm_node266__m.layer-3.gamma" {noinline} = dense<5.000000e-01> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node267__m.layer-3.beta" {noinline} = dense<0.333333343> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node268__m.layer-3.moving_mean" {noinline} = dense<2.500000e-01> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node269__m.layer-3.moving_variance" {noinline} = dense<2.000000e-01> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node288__m.layer-9.depthwise_kernel" {noinline} = dense<0.166666672> : tensor<3x3x16x1xf32> |
| util.global private @"__iree_flow___sm_node294__m.layer-10.gamma" {noinline} = dense<0.142857149> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node295__m.layer-10.beta" {noinline} = dense<1.250000e-01> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node296__m.layer-10.moving_mean" {noinline} = dense<0.111111112> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node297__m.layer-10.moving_variance" {noinline} = dense<1.000000e-01> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node314__m.layer-14.kernel" {noinline} = dense<0.0909090936> : tensor<1x1x16x8xf32> |
| util.global private @"__iree_flow___sm_node315__m.layer-14.bias" {noinline} = dense<0.0833333358> : tensor<8xf32> |
| util.global private @"__iree_flow___sm_node324__m.layer-16.kernel" {noinline} = dense<0.0769230798> : tensor<1x1x8x16xf32> |
| util.global private @"__iree_flow___sm_node325__m.layer-16.bias" {noinline} = dense<0.0714285746> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node340__m.layer-21.kernel" {noinline} = dense<0.0666666701> : tensor<1x1x16x16xf32> |
| util.global private @"__iree_flow___sm_node346__m.layer-22.gamma" {noinline} = dense<6.250000e-02> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node347__m.layer-22.beta" {noinline} = dense<0.0588235296> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node348__m.layer-22.moving_mean" {noinline} = dense<0.055555556> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node349__m.layer-22.moving_variance" {noinline} = dense<0.0526315793> : tensor<16xf32> |
| util.global private @"__iree_flow___sm_node354__m.layer-23.kernel" {noinline} = dense<5.000000e-02> : tensor<1x1x16x72xf32> |
| util.global private @"__iree_flow___sm_node360__m.layer-24.gamma" {noinline} = dense<0.0476190485> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node361__m.layer-24.beta" {noinline} = dense<0.0454545468> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node362__m.layer-24.moving_mean" {noinline} = dense<0.0434782617> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node363__m.layer-24.moving_variance" {noinline} = dense<0.0416666679> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node376__m.layer-27.depthwise_kernel" {noinline} = dense<4.000000e-02> : tensor<3x3x72x1xf32> |
| util.global private @"__iree_flow___sm_node382__m.layer-28.gamma" {noinline} = dense<0.0384615399> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node383__m.layer-28.beta" {noinline} = dense<0.0370370373> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node384__m.layer-28.moving_mean" {noinline} = dense<0.0357142873> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node385__m.layer-28.moving_variance" {noinline} = dense<0.0344827585> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node394__m.layer-30.kernel" {noinline} = dense<0.0333333351> : tensor<1x1x72x24xf32> |
| util.global private @"__iree_flow___sm_node400__m.layer-31.gamma" {noinline} = dense<0.0322580636> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node401__m.layer-31.beta" {noinline} = dense<3.125000e-02> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node402__m.layer-31.moving_mean" {noinline} = dense<0.0303030312> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node403__m.layer-31.moving_variance" {noinline} = dense<0.0294117648> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node408__m.layer-32.kernel" {noinline} = dense<0.0285714287> : tensor<1x1x24x88xf32> |
| util.global private @"__iree_flow___sm_node414__m.layer-33.gamma" {noinline} = dense<0.027777778> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node415__m.layer-33.beta" {noinline} = dense<0.0270270277> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node416__m.layer-33.moving_mean" {noinline} = dense<0.0263157897> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node417__m.layer-33.moving_variance" {noinline} = dense<0.025641026> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node426__m.layer-35.depthwise_kernel" {noinline} = dense<2.500000e-02> : tensor<3x3x88x1xf32> |
| util.global private @"__iree_flow___sm_node432__m.layer-36.gamma" {noinline} = dense<0.024390243> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node433__m.layer-36.beta" {noinline} = dense<0.0238095243> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node434__m.layer-36.moving_mean" {noinline} = dense<0.0232558139> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node435__m.layer-36.moving_variance" {noinline} = dense<0.0227272734> : tensor<88xf32> |
| util.global private @"__iree_flow___sm_node444__m.layer-38.kernel" {noinline} = dense<0.0222222228> : tensor<1x1x88x24xf32> |
| util.global private @"__iree_flow___sm_node450__m.layer-39.gamma" {noinline} = dense<0.0217391308> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node451__m.layer-39.beta" {noinline} = dense<0.0212765951> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node452__m.layer-39.moving_mean" {noinline} = dense<0.020833334> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node453__m.layer-39.moving_variance" {noinline} = dense<0.0204081628> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node462__m.layer-41.kernel" {noinline} = dense<2.000000e-02> : tensor<1x1x24x96xf32> |
| util.global private @"__iree_flow___sm_node468__m.layer-42.gamma" {noinline} = dense<0.0196078438> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node469__m.layer-42.beta" {noinline} = dense<0.0192307699> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node470__m.layer-42.moving_mean" {noinline} = dense<0.0188679248> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node471__m.layer-42.moving_variance" {noinline} = dense<0.0185185187> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node490__m.layer-48.depthwise_kernel" {noinline} = dense<0.0181818176> : tensor<5x5x96x1xf32> |
| util.global private @"__iree_flow___sm_node496__m.layer-49.gamma" {noinline} = dense<0.0178571437> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node497__m.layer-49.beta" {noinline} = dense<0.0175438598> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node498__m.layer-49.moving_mean" {noinline} = dense<0.0172413792> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node499__m.layer-49.moving_variance" {noinline} = dense<0.0169491526> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node522__m.layer-56.kernel" {noinline} = dense<0.0166666675> : tensor<1x1x96x24xf32> |
| util.global private @"__iree_flow___sm_node523__m.layer-56.bias" {noinline} = dense<0.0163934417> : tensor<24xf32> |
| util.global private @"__iree_flow___sm_node532__m.layer-58.kernel" {noinline} = dense<0.0161290318> : tensor<1x1x24x96xf32> |
| util.global private @"__iree_flow___sm_node533__m.layer-58.bias" {noinline} = dense<0.0158730168> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node548__m.layer-63.kernel" {noinline} = dense<1.562500e-02> : tensor<1x1x96x40xf32> |
| util.global private @"__iree_flow___sm_node554__m.layer-64.gamma" {noinline} = dense<0.0153846154> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node555__m.layer-64.beta" {noinline} = dense<0.0151515156> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node556__m.layer-64.moving_mean" {noinline} = dense<0.0149253728> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node557__m.layer-64.moving_variance" {noinline} = dense<0.0147058824> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node562__m.layer-65.kernel" {noinline} = dense<0.0144927539> : tensor<1x1x40x240xf32> |
| util.global private @"__iree_flow___sm_node568__m.layer-66.gamma" {noinline} = dense<0.0142857144> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node569__m.layer-66.beta" {noinline} = dense<0.0140845068> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node570__m.layer-66.moving_mean" {noinline} = dense<0.013888889> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node571__m.layer-66.moving_variance" {noinline} = dense<0.01369863> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node586__m.layer-71.depthwise_kernel" {noinline} = dense<0.0135135138> : tensor<5x5x240x1xf32> |
| util.global private @"__iree_flow___sm_node592__m.layer-72.gamma" {noinline} = dense<0.0133333337> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node593__m.layer-72.beta" {noinline} = dense<0.0131578948> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node594__m.layer-72.moving_mean" {noinline} = dense<0.012987013> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node595__m.layer-72.moving_variance" {noinline} = dense<0.012820513> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node618__m.layer-79.kernel" {noinline} = dense<0.0126582282> : tensor<1x1x240x64xf32> |
| util.global private @"__iree_flow___sm_node619__m.layer-79.bias" {noinline} = dense<1.250000e-02> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node628__m.layer-81.kernel" {noinline} = dense<0.0123456791> : tensor<1x1x64x240xf32> |
| util.global private @"__iree_flow___sm_node629__m.layer-81.bias" {noinline} = dense<0.0121951215> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node644__m.layer-86.kernel" {noinline} = dense<0.0120481923> : tensor<1x1x240x40xf32> |
| util.global private @"__iree_flow___sm_node650__m.layer-87.gamma" {noinline} = dense<0.0119047621> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node651__m.layer-87.beta" {noinline} = dense<0.0117647061> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node652__m.layer-87.moving_mean" {noinline} = dense<0.0116279069> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node653__m.layer-87.moving_variance" {noinline} = dense<0.0114942528> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node662__m.layer-89.kernel" {noinline} = dense<0.0113636367> : tensor<1x1x40x240xf32> |
| util.global private @"__iree_flow___sm_node668__m.layer-90.gamma" {noinline} = dense<0.0112359552> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node669__m.layer-90.beta" {noinline} = dense<0.0111111114> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node670__m.layer-90.moving_mean" {noinline} = dense<0.0109890113> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node671__m.layer-90.moving_variance" {noinline} = dense<0.0108695654> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node686__m.layer-95.depthwise_kernel" {noinline} = dense<0.0107526882> : tensor<5x5x240x1xf32> |
| util.global private @"__iree_flow___sm_node692__m.layer-96.gamma" {noinline} = dense<0.0106382975> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node693__m.layer-96.beta" {noinline} = dense<0.0105263162> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node694__m.layer-96.moving_mean" {noinline} = dense<0.010416667> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node695__m.layer-96.moving_variance" {noinline} = dense<0.010309278> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node718__m.layer-103.kernel" {noinline} = dense<0.0102040814> : tensor<1x1x240x64xf32> |
| util.global private @"__iree_flow___sm_node719__m.layer-103.bias" {noinline} = dense<0.0101010101> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node728__m.layer-105.kernel" {noinline} = dense<0.00999999977> : tensor<1x1x64x240xf32> |
| util.global private @"__iree_flow___sm_node729__m.layer-105.bias" {noinline} = dense<9.900990e-03> : tensor<240xf32> |
| util.global private @"__iree_flow___sm_node744__m.layer-110.kernel" {noinline} = dense<0.00980392192> : tensor<1x1x240x40xf32> |
| util.global private @"__iree_flow___sm_node750__m.layer-111.gamma" {noinline} = dense<0.00970873795> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node751__m.layer-111.beta" {noinline} = dense<0.00961538497> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node752__m.layer-111.moving_mean" {noinline} = dense<9.523810e-03> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node753__m.layer-111.moving_variance" {noinline} = dense<0.0094339624> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node762__m.layer-113.kernel" {noinline} = dense<0.00934579409> : tensor<1x1x40x120xf32> |
| util.global private @"__iree_flow___sm_node768__m.layer-114.gamma" {noinline} = dense<0.00925925932> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node769__m.layer-114.beta" {noinline} = dense<0.00917431153> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node770__m.layer-114.moving_mean" {noinline} = dense<0.0090909088> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node771__m.layer-114.moving_variance" {noinline} = dense<0.00900900922> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node786__m.layer-119.depthwise_kernel" {noinline} = dense<0.00892857183> : tensor<5x5x120x1xf32> |
| util.global private @"__iree_flow___sm_node792__m.layer-120.gamma" {noinline} = dense<0.00884955748> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node793__m.layer-120.beta" {noinline} = dense<0.00877192988> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node794__m.layer-120.moving_mean" {noinline} = dense<0.00869565178> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node795__m.layer-120.moving_variance" {noinline} = dense<8.620690e-03> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node818__m.layer-127.kernel" {noinline} = dense<0.00854700897> : tensor<1x1x120x32xf32> |
| util.global private @"__iree_flow___sm_node819__m.layer-127.bias" {noinline} = dense<0.00847457629> : tensor<32xf32> |
| util.global private @"__iree_flow___sm_node828__m.layer-129.kernel" {noinline} = dense<0.00840336177> : tensor<1x1x32x120xf32> |
| util.global private @"__iree_flow___sm_node829__m.layer-129.bias" {noinline} = dense<0.00833333377> : tensor<120xf32> |
| util.global private @"__iree_flow___sm_node844__m.layer-134.kernel" {noinline} = dense<0.00826446246> : tensor<1x1x120x48xf32> |
| util.global private @"__iree_flow___sm_node850__m.layer-135.gamma" {noinline} = dense<0.00819672085> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node851__m.layer-135.beta" {noinline} = dense<0.008130081> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node852__m.layer-135.moving_mean" {noinline} = dense<0.00806451589> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node853__m.layer-135.moving_variance" {noinline} = dense<8.000000e-03> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node858__m.layer-136.kernel" {noinline} = dense<0.00793650839> : tensor<1x1x48x144xf32> |
| util.global private @"__iree_flow___sm_node864__m.layer-137.gamma" {noinline} = dense<0.00787401571> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node865__m.layer-137.beta" {noinline} = dense<7.812500e-03> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node866__m.layer-137.moving_mean" {noinline} = dense<0.00775193795> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node867__m.layer-137.moving_variance" {noinline} = dense<0.0076923077> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node882__m.layer-142.depthwise_kernel" {noinline} = dense<0.00763358781> : tensor<5x5x144x1xf32> |
| util.global private @"__iree_flow___sm_node888__m.layer-143.gamma" {noinline} = dense<0.0075757578> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node889__m.layer-143.beta" {noinline} = dense<0.00751879718> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node890__m.layer-143.moving_mean" {noinline} = dense<0.00746268639> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node891__m.layer-143.moving_variance" {noinline} = dense<0.00740740728> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node914__m.layer-150.kernel" {noinline} = dense<0.0073529412> : tensor<1x1x144x40xf32> |
| util.global private @"__iree_flow___sm_node915__m.layer-150.bias" {noinline} = dense<7.299270e-03> : tensor<40xf32> |
| util.global private @"__iree_flow___sm_node924__m.layer-152.kernel" {noinline} = dense<0.00724637694> : tensor<1x1x40x144xf32> |
| util.global private @"__iree_flow___sm_node925__m.layer-152.bias" {noinline} = dense<0.00719424477> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node940__m.layer-157.kernel" {noinline} = dense<0.00714285718> : tensor<1x1x144x48xf32> |
| util.global private @"__iree_flow___sm_node946__m.layer-158.gamma" {noinline} = dense<0.00709219835> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node947__m.layer-158.beta" {noinline} = dense<0.00704225338> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node948__m.layer-158.moving_mean" {noinline} = dense<0.00699300691> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node949__m.layer-158.moving_variance" {noinline} = dense<0.0069444445> : tensor<48xf32> |
| util.global private @"__iree_flow___sm_node958__m.layer-160.kernel" {noinline} = dense<0.0068965517> : tensor<1x1x48x288xf32> |
| util.global private @"__iree_flow___sm_node964__m.layer-161.gamma" {noinline} = dense<0.00684931502> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node965__m.layer-161.beta" {noinline} = dense<0.00680272094> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node966__m.layer-161.moving_mean" {noinline} = dense<0.00675675692> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node967__m.layer-161.moving_variance" {noinline} = dense<0.00671140943> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node986__m.layer-167.depthwise_kernel" {noinline} = dense<0.00666666683> : tensor<5x5x288x1xf32> |
| util.global private @"__iree_flow___sm_node992__m.layer-168.gamma" {noinline} = dense<0.00662251655> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node993__m.layer-168.beta" {noinline} = dense<0.00657894742> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node994__m.layer-168.moving_mean" {noinline} = dense<0.00653594779> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node995__m.layer-168.moving_variance" {noinline} = dense<0.00649350649> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node1018__m.layer-175.kernel" {noinline} = dense<0.0064516128> : tensor<1x1x288x72xf32> |
| util.global private @"__iree_flow___sm_node1019__m.layer-175.bias" {noinline} = dense<0.00641025649> : tensor<72xf32> |
| util.global private @"__iree_flow___sm_node1028__m.layer-177.kernel" {noinline} = dense<0.00636942684> : tensor<1x1x72x288xf32> |
| util.global private @"__iree_flow___sm_node1029__m.layer-177.bias" {noinline} = dense<0.00632911408> : tensor<288xf32> |
| util.global private @"__iree_flow___sm_node1044__m.layer-182.kernel" {noinline} = dense<0.00628930796> : tensor<1x1x288x96xf32> |
| util.global private @"__iree_flow___sm_node1050__m.layer-183.gamma" {noinline} = dense<6.250000e-03> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1051__m.layer-183.beta" {noinline} = dense<0.00621118024> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1052__m.layer-183.moving_mean" {noinline} = dense<0.00617283955> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1053__m.layer-183.moving_variance" {noinline} = dense<0.00613496918> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1058__m.layer-184.kernel" {noinline} = dense<0.00609756075> : tensor<1x1x96x576xf32> |
| util.global private @"__iree_flow___sm_node1064__m.layer-185.gamma" {noinline} = dense<0.00606060587> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1065__m.layer-185.beta" {noinline} = dense<0.00602409616> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1066__m.layer-185.moving_mean" {noinline} = dense<0.00598802418> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1067__m.layer-185.moving_variance" {noinline} = dense<0.00595238106> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1082__m.layer-190.depthwise_kernel" {noinline} = dense<5.917160e-03> : tensor<5x5x576x1xf32> |
| util.global private @"__iree_flow___sm_node1088__m.layer-191.gamma" {noinline} = dense<0.00588235306> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1089__m.layer-191.beta" {noinline} = dense<0.00584795326> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1090__m.layer-191.moving_mean" {noinline} = dense<0.00581395347> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1091__m.layer-191.moving_variance" {noinline} = dense<0.00578034669> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1114__m.layer-198.kernel" {noinline} = dense<0.00574712642> : tensor<1x1x576x144xf32> |
| util.global private @"__iree_flow___sm_node1115__m.layer-198.bias" {noinline} = dense<0.00571428565> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node1124__m.layer-200.kernel" {noinline} = dense<0.00568181835> : tensor<1x1x144x576xf32> |
| util.global private @"__iree_flow___sm_node1125__m.layer-200.bias" {noinline} = dense<0.00564971752> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1140__m.layer-205.kernel" {noinline} = dense<0.00561797759> : tensor<1x1x576x96xf32> |
| util.global private @"__iree_flow___sm_node1146__m.layer-206.gamma" {noinline} = dense<0.00558659201> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1147__m.layer-206.beta" {noinline} = dense<0.00555555569> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1148__m.layer-206.moving_mean" {noinline} = dense<0.00552486209> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1149__m.layer-206.moving_variance" {noinline} = dense<0.00549450563> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1158__m.layer-208.kernel" {noinline} = dense<0.00546448072> : tensor<1x1x96x576xf32> |
| util.global private @"__iree_flow___sm_node1164__m.layer-209.gamma" {noinline} = dense<0.00543478271> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1165__m.layer-209.beta" {noinline} = dense<0.00540540554> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1166__m.layer-209.moving_mean" {noinline} = dense<0.00537634408> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1167__m.layer-209.moving_variance" {noinline} = dense<0.00534759369> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1182__m.layer-214.depthwise_kernel" {noinline} = dense<0.00531914877> : tensor<5x5x576x1xf32> |
| util.global private @"__iree_flow___sm_node1188__m.layer-215.gamma" {noinline} = dense<0.00529100513> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1189__m.layer-215.beta" {noinline} = dense<0.00526315812> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1190__m.layer-215.moving_mean" {noinline} = dense<0.00523560215> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1191__m.layer-215.moving_variance" {noinline} = dense<0.00520833349> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1214__m.layer-222.kernel" {noinline} = dense<0.00518134702> : tensor<1x1x576x144xf32> |
| util.global private @"__iree_flow___sm_node1215__m.layer-222.bias" {noinline} = dense<0.00515463902> : tensor<144xf32> |
| util.global private @"__iree_flow___sm_node1224__m.layer-224.kernel" {noinline} = dense<0.00512820529> : tensor<1x1x144x576xf32> |
| util.global private @"__iree_flow___sm_node1225__m.layer-224.bias" {noinline} = dense<0.00510204071> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1240__m.layer-229.kernel" {noinline} = dense<0.00507614203> : tensor<1x1x576x96xf32> |
| util.global private @"__iree_flow___sm_node1246__m.layer-230.gamma" {noinline} = dense<0.00505050505> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1247__m.layer-230.beta" {noinline} = dense<0.00502512557> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1248__m.layer-230.moving_mean" {noinline} = dense<5.000000e-03> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1249__m.layer-230.moving_variance" {noinline} = dense<0.00497512426> : tensor<96xf32> |
| util.global private @"__iree_flow___sm_node1258__m.layer-232.kernel" {noinline} = dense<0.00495049497> : tensor<1x1x96x576xf32> |
| util.global private @"__iree_flow___sm_node1264__m.layer-233.gamma" {noinline} = dense<0.00492610829> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1265__m.layer-233.beta" {noinline} = dense<0.00490196096> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1266__m.layer-233.moving_mean" {noinline} = dense<0.00487804879> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1267__m.layer-233.moving_variance" {noinline} = dense<0.00485436898> : tensor<576xf32> |
| util.global private @"__iree_flow___sm_node1290__m.layer-240.kernel" {noinline} = dense<0.00483091781> : tensor<1x1x576x1024xf32> |
| util.global private @"__iree_flow___sm_node1291__m.layer-240.bias" {noinline} = dense<0.00480769249> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1310__m.layer-246.kernel" {noinline} = dense<0.00478468882> : tensor<1x1x1024x1000xf32> |
| util.global private @"__iree_flow___sm_node1311__m.layer-246.bias" {noinline} = dense<0.00476190494> : tensor<1000xf32> |
| func.func @call() { |
| %arg0 = util.unfoldable_constant dense<1.5> : tensor<1x224x224x3xf32> |
| %0 = util.global.address @"__iree_flow___sm_node260__m.layer-2.kernel" : !util.ptr<tensor<3x3x3x16xf32>> |
| %1 = util.global.address @"__iree_flow___sm_node266__m.layer-3.gamma" : !util.ptr<tensor<16xf32>> |
| %2 = util.global.address @"__iree_flow___sm_node267__m.layer-3.beta" : !util.ptr<tensor<16xf32>> |
| %3 = util.global.address @"__iree_flow___sm_node268__m.layer-3.moving_mean" : !util.ptr<tensor<16xf32>> |
| %4 = util.global.address @"__iree_flow___sm_node269__m.layer-3.moving_variance" : !util.ptr<tensor<16xf32>> |
| %5 = util.global.address @"__iree_flow___sm_node288__m.layer-9.depthwise_kernel" : !util.ptr<tensor<3x3x16x1xf32>> |
| %6 = util.global.address @"__iree_flow___sm_node294__m.layer-10.gamma" : !util.ptr<tensor<16xf32>> |
| %7 = util.global.address @"__iree_flow___sm_node295__m.layer-10.beta" : !util.ptr<tensor<16xf32>> |
| %8 = util.global.address @"__iree_flow___sm_node296__m.layer-10.moving_mean" : !util.ptr<tensor<16xf32>> |
| %9 = util.global.address @"__iree_flow___sm_node297__m.layer-10.moving_variance" : !util.ptr<tensor<16xf32>> |
| %10 = util.global.address @"__iree_flow___sm_node314__m.layer-14.kernel" : !util.ptr<tensor<1x1x16x8xf32>> |
| %11 = util.global.address @"__iree_flow___sm_node315__m.layer-14.bias" : !util.ptr<tensor<8xf32>> |
| %12 = util.global.address @"__iree_flow___sm_node324__m.layer-16.kernel" : !util.ptr<tensor<1x1x8x16xf32>> |
| %13 = util.global.address @"__iree_flow___sm_node325__m.layer-16.bias" : !util.ptr<tensor<16xf32>> |
| %14 = util.global.address @"__iree_flow___sm_node340__m.layer-21.kernel" : !util.ptr<tensor<1x1x16x16xf32>> |
| %15 = util.global.address @"__iree_flow___sm_node346__m.layer-22.gamma" : !util.ptr<tensor<16xf32>> |
| %16 = util.global.address @"__iree_flow___sm_node347__m.layer-22.beta" : !util.ptr<tensor<16xf32>> |
| %17 = util.global.address @"__iree_flow___sm_node348__m.layer-22.moving_mean" : !util.ptr<tensor<16xf32>> |
| %18 = util.global.address @"__iree_flow___sm_node349__m.layer-22.moving_variance" : !util.ptr<tensor<16xf32>> |
| %19 = util.global.address @"__iree_flow___sm_node354__m.layer-23.kernel" : !util.ptr<tensor<1x1x16x72xf32>> |
| %20 = util.global.address @"__iree_flow___sm_node360__m.layer-24.gamma" : !util.ptr<tensor<72xf32>> |
| %21 = util.global.address @"__iree_flow___sm_node361__m.layer-24.beta" : !util.ptr<tensor<72xf32>> |
| %22 = util.global.address @"__iree_flow___sm_node362__m.layer-24.moving_mean" : !util.ptr<tensor<72xf32>> |
| %23 = util.global.address @"__iree_flow___sm_node363__m.layer-24.moving_variance" : !util.ptr<tensor<72xf32>> |
| %24 = util.global.address @"__iree_flow___sm_node376__m.layer-27.depthwise_kernel" : !util.ptr<tensor<3x3x72x1xf32>> |
| %25 = util.global.address @"__iree_flow___sm_node382__m.layer-28.gamma" : !util.ptr<tensor<72xf32>> |
| %26 = util.global.address @"__iree_flow___sm_node383__m.layer-28.beta" : !util.ptr<tensor<72xf32>> |
| %27 = util.global.address @"__iree_flow___sm_node384__m.layer-28.moving_mean" : !util.ptr<tensor<72xf32>> |
| %28 = util.global.address @"__iree_flow___sm_node385__m.layer-28.moving_variance" : !util.ptr<tensor<72xf32>> |
| %29 = util.global.address @"__iree_flow___sm_node394__m.layer-30.kernel" : !util.ptr<tensor<1x1x72x24xf32>> |
| %30 = util.global.address @"__iree_flow___sm_node400__m.layer-31.gamma" : !util.ptr<tensor<24xf32>> |
| %31 = util.global.address @"__iree_flow___sm_node401__m.layer-31.beta" : !util.ptr<tensor<24xf32>> |
| %32 = util.global.address @"__iree_flow___sm_node402__m.layer-31.moving_mean" : !util.ptr<tensor<24xf32>> |
| %33 = util.global.address @"__iree_flow___sm_node403__m.layer-31.moving_variance" : !util.ptr<tensor<24xf32>> |
| %34 = util.global.address @"__iree_flow___sm_node408__m.layer-32.kernel" : !util.ptr<tensor<1x1x24x88xf32>> |
| %35 = util.global.address @"__iree_flow___sm_node414__m.layer-33.gamma" : !util.ptr<tensor<88xf32>> |
| %36 = util.global.address @"__iree_flow___sm_node415__m.layer-33.beta" : !util.ptr<tensor<88xf32>> |
| %37 = util.global.address @"__iree_flow___sm_node416__m.layer-33.moving_mean" : !util.ptr<tensor<88xf32>> |
| %38 = util.global.address @"__iree_flow___sm_node417__m.layer-33.moving_variance" : !util.ptr<tensor<88xf32>> |
| %39 = util.global.address @"__iree_flow___sm_node426__m.layer-35.depthwise_kernel" : !util.ptr<tensor<3x3x88x1xf32>> |
| %40 = util.global.address @"__iree_flow___sm_node432__m.layer-36.gamma" : !util.ptr<tensor<88xf32>> |
| %41 = util.global.address @"__iree_flow___sm_node433__m.layer-36.beta" : !util.ptr<tensor<88xf32>> |
| %42 = util.global.address @"__iree_flow___sm_node434__m.layer-36.moving_mean" : !util.ptr<tensor<88xf32>> |
| %43 = util.global.address @"__iree_flow___sm_node435__m.layer-36.moving_variance" : !util.ptr<tensor<88xf32>> |
| %44 = util.global.address @"__iree_flow___sm_node444__m.layer-38.kernel" : !util.ptr<tensor<1x1x88x24xf32>> |
| %45 = util.global.address @"__iree_flow___sm_node450__m.layer-39.gamma" : !util.ptr<tensor<24xf32>> |
| %46 = util.global.address @"__iree_flow___sm_node451__m.layer-39.beta" : !util.ptr<tensor<24xf32>> |
| %47 = util.global.address @"__iree_flow___sm_node452__m.layer-39.moving_mean" : !util.ptr<tensor<24xf32>> |
| %48 = util.global.address @"__iree_flow___sm_node453__m.layer-39.moving_variance" : !util.ptr<tensor<24xf32>> |
| %49 = util.global.address @"__iree_flow___sm_node462__m.layer-41.kernel" : !util.ptr<tensor<1x1x24x96xf32>> |
| %50 = util.global.address @"__iree_flow___sm_node468__m.layer-42.gamma" : !util.ptr<tensor<96xf32>> |
| %51 = util.global.address @"__iree_flow___sm_node469__m.layer-42.beta" : !util.ptr<tensor<96xf32>> |
| %52 = util.global.address @"__iree_flow___sm_node470__m.layer-42.moving_mean" : !util.ptr<tensor<96xf32>> |
| %53 = util.global.address @"__iree_flow___sm_node471__m.layer-42.moving_variance" : !util.ptr<tensor<96xf32>> |
| %54 = util.global.address @"__iree_flow___sm_node490__m.layer-48.depthwise_kernel" : !util.ptr<tensor<5x5x96x1xf32>> |
| %55 = util.global.address @"__iree_flow___sm_node496__m.layer-49.gamma" : !util.ptr<tensor<96xf32>> |
| %56 = util.global.address @"__iree_flow___sm_node497__m.layer-49.beta" : !util.ptr<tensor<96xf32>> |
| %57 = util.global.address @"__iree_flow___sm_node498__m.layer-49.moving_mean" : !util.ptr<tensor<96xf32>> |
| %58 = util.global.address @"__iree_flow___sm_node499__m.layer-49.moving_variance" : !util.ptr<tensor<96xf32>> |
| %59 = util.global.address @"__iree_flow___sm_node522__m.layer-56.kernel" : !util.ptr<tensor<1x1x96x24xf32>> |
| %60 = util.global.address @"__iree_flow___sm_node523__m.layer-56.bias" : !util.ptr<tensor<24xf32>> |
| %61 = util.global.address @"__iree_flow___sm_node532__m.layer-58.kernel" : !util.ptr<tensor<1x1x24x96xf32>> |
| %62 = util.global.address @"__iree_flow___sm_node533__m.layer-58.bias" : !util.ptr<tensor<96xf32>> |
| %63 = util.global.address @"__iree_flow___sm_node548__m.layer-63.kernel" : !util.ptr<tensor<1x1x96x40xf32>> |
| %64 = util.global.address @"__iree_flow___sm_node554__m.layer-64.gamma" : !util.ptr<tensor<40xf32>> |
| %65 = util.global.address @"__iree_flow___sm_node555__m.layer-64.beta" : !util.ptr<tensor<40xf32>> |
| %66 = util.global.address @"__iree_flow___sm_node556__m.layer-64.moving_mean" : !util.ptr<tensor<40xf32>> |
| %67 = util.global.address @"__iree_flow___sm_node557__m.layer-64.moving_variance" : !util.ptr<tensor<40xf32>> |
| %68 = util.global.address @"__iree_flow___sm_node562__m.layer-65.kernel" : !util.ptr<tensor<1x1x40x240xf32>> |
| %69 = util.global.address @"__iree_flow___sm_node568__m.layer-66.gamma" : !util.ptr<tensor<240xf32>> |
| %70 = util.global.address @"__iree_flow___sm_node569__m.layer-66.beta" : !util.ptr<tensor<240xf32>> |
| %71 = util.global.address @"__iree_flow___sm_node570__m.layer-66.moving_mean" : !util.ptr<tensor<240xf32>> |
| %72 = util.global.address @"__iree_flow___sm_node571__m.layer-66.moving_variance" : !util.ptr<tensor<240xf32>> |
| %73 = util.global.address @"__iree_flow___sm_node586__m.layer-71.depthwise_kernel" : !util.ptr<tensor<5x5x240x1xf32>> |
| %74 = util.global.address @"__iree_flow___sm_node592__m.layer-72.gamma" : !util.ptr<tensor<240xf32>> |
| %75 = util.global.address @"__iree_flow___sm_node593__m.layer-72.beta" : !util.ptr<tensor<240xf32>> |
| %76 = util.global.address @"__iree_flow___sm_node594__m.layer-72.moving_mean" : !util.ptr<tensor<240xf32>> |
| %77 = util.global.address @"__iree_flow___sm_node595__m.layer-72.moving_variance" : !util.ptr<tensor<240xf32>> |
| %78 = util.global.address @"__iree_flow___sm_node618__m.layer-79.kernel" : !util.ptr<tensor<1x1x240x64xf32>> |
| %79 = util.global.address @"__iree_flow___sm_node619__m.layer-79.bias" : !util.ptr<tensor<64xf32>> |
| %80 = util.global.address @"__iree_flow___sm_node628__m.layer-81.kernel" : !util.ptr<tensor<1x1x64x240xf32>> |
| %81 = util.global.address @"__iree_flow___sm_node629__m.layer-81.bias" : !util.ptr<tensor<240xf32>> |
| %82 = util.global.address @"__iree_flow___sm_node644__m.layer-86.kernel" : !util.ptr<tensor<1x1x240x40xf32>> |
| %83 = util.global.address @"__iree_flow___sm_node650__m.layer-87.gamma" : !util.ptr<tensor<40xf32>> |
| %84 = util.global.address @"__iree_flow___sm_node651__m.layer-87.beta" : !util.ptr<tensor<40xf32>> |
| %85 = util.global.address @"__iree_flow___sm_node652__m.layer-87.moving_mean" : !util.ptr<tensor<40xf32>> |
| %86 = util.global.address @"__iree_flow___sm_node653__m.layer-87.moving_variance" : !util.ptr<tensor<40xf32>> |
| %87 = util.global.address @"__iree_flow___sm_node662__m.layer-89.kernel" : !util.ptr<tensor<1x1x40x240xf32>> |
| %88 = util.global.address @"__iree_flow___sm_node668__m.layer-90.gamma" : !util.ptr<tensor<240xf32>> |
| %89 = util.global.address @"__iree_flow___sm_node669__m.layer-90.beta" : !util.ptr<tensor<240xf32>> |
| %90 = util.global.address @"__iree_flow___sm_node670__m.layer-90.moving_mean" : !util.ptr<tensor<240xf32>> |
| %91 = util.global.address @"__iree_flow___sm_node671__m.layer-90.moving_variance" : !util.ptr<tensor<240xf32>> |
| %92 = util.global.address @"__iree_flow___sm_node686__m.layer-95.depthwise_kernel" : !util.ptr<tensor<5x5x240x1xf32>> |
| %93 = util.global.address @"__iree_flow___sm_node692__m.layer-96.gamma" : !util.ptr<tensor<240xf32>> |
| %94 = util.global.address @"__iree_flow___sm_node693__m.layer-96.beta" : !util.ptr<tensor<240xf32>> |
| %95 = util.global.address @"__iree_flow___sm_node694__m.layer-96.moving_mean" : !util.ptr<tensor<240xf32>> |
| %96 = util.global.address @"__iree_flow___sm_node695__m.layer-96.moving_variance" : !util.ptr<tensor<240xf32>> |
| %97 = util.global.address @"__iree_flow___sm_node718__m.layer-103.kernel" : !util.ptr<tensor<1x1x240x64xf32>> |
| %98 = util.global.address @"__iree_flow___sm_node719__m.layer-103.bias" : !util.ptr<tensor<64xf32>> |
| %99 = util.global.address @"__iree_flow___sm_node728__m.layer-105.kernel" : !util.ptr<tensor<1x1x64x240xf32>> |
| %100 = util.global.address @"__iree_flow___sm_node729__m.layer-105.bias" : !util.ptr<tensor<240xf32>> |
| %101 = util.global.address @"__iree_flow___sm_node744__m.layer-110.kernel" : !util.ptr<tensor<1x1x240x40xf32>> |
| %102 = util.global.address @"__iree_flow___sm_node750__m.layer-111.gamma" : !util.ptr<tensor<40xf32>> |
| %103 = util.global.address @"__iree_flow___sm_node751__m.layer-111.beta" : !util.ptr<tensor<40xf32>> |
| %104 = util.global.address @"__iree_flow___sm_node752__m.layer-111.moving_mean" : !util.ptr<tensor<40xf32>> |
| %105 = util.global.address @"__iree_flow___sm_node753__m.layer-111.moving_variance" : !util.ptr<tensor<40xf32>> |
| %106 = util.global.address @"__iree_flow___sm_node762__m.layer-113.kernel" : !util.ptr<tensor<1x1x40x120xf32>> |
| %107 = util.global.address @"__iree_flow___sm_node768__m.layer-114.gamma" : !util.ptr<tensor<120xf32>> |
| %108 = util.global.address @"__iree_flow___sm_node769__m.layer-114.beta" : !util.ptr<tensor<120xf32>> |
| %109 = util.global.address @"__iree_flow___sm_node770__m.layer-114.moving_mean" : !util.ptr<tensor<120xf32>> |
| %110 = util.global.address @"__iree_flow___sm_node771__m.layer-114.moving_variance" : !util.ptr<tensor<120xf32>> |
| %111 = util.global.address @"__iree_flow___sm_node786__m.layer-119.depthwise_kernel" : !util.ptr<tensor<5x5x120x1xf32>> |
| %112 = util.global.address @"__iree_flow___sm_node792__m.layer-120.gamma" : !util.ptr<tensor<120xf32>> |
| %113 = util.global.address @"__iree_flow___sm_node793__m.layer-120.beta" : !util.ptr<tensor<120xf32>> |
| %114 = util.global.address @"__iree_flow___sm_node794__m.layer-120.moving_mean" : !util.ptr<tensor<120xf32>> |
| %115 = util.global.address @"__iree_flow___sm_node795__m.layer-120.moving_variance" : !util.ptr<tensor<120xf32>> |
| %116 = util.global.address @"__iree_flow___sm_node818__m.layer-127.kernel" : !util.ptr<tensor<1x1x120x32xf32>> |
| %117 = util.global.address @"__iree_flow___sm_node819__m.layer-127.bias" : !util.ptr<tensor<32xf32>> |
| %118 = util.global.address @"__iree_flow___sm_node828__m.layer-129.kernel" : !util.ptr<tensor<1x1x32x120xf32>> |
| %119 = util.global.address @"__iree_flow___sm_node829__m.layer-129.bias" : !util.ptr<tensor<120xf32>> |
| %120 = util.global.address @"__iree_flow___sm_node844__m.layer-134.kernel" : !util.ptr<tensor<1x1x120x48xf32>> |
| %121 = util.global.address @"__iree_flow___sm_node850__m.layer-135.gamma" : !util.ptr<tensor<48xf32>> |
| %122 = util.global.address @"__iree_flow___sm_node851__m.layer-135.beta" : !util.ptr<tensor<48xf32>> |
| %123 = util.global.address @"__iree_flow___sm_node852__m.layer-135.moving_mean" : !util.ptr<tensor<48xf32>> |
| %124 = util.global.address @"__iree_flow___sm_node853__m.layer-135.moving_variance" : !util.ptr<tensor<48xf32>> |
| %125 = util.global.address @"__iree_flow___sm_node858__m.layer-136.kernel" : !util.ptr<tensor<1x1x48x144xf32>> |
| %126 = util.global.address @"__iree_flow___sm_node864__m.layer-137.gamma" : !util.ptr<tensor<144xf32>> |
| %127 = util.global.address @"__iree_flow___sm_node865__m.layer-137.beta" : !util.ptr<tensor<144xf32>> |
| %128 = util.global.address @"__iree_flow___sm_node866__m.layer-137.moving_mean" : !util.ptr<tensor<144xf32>> |
| %129 = util.global.address @"__iree_flow___sm_node867__m.layer-137.moving_variance" : !util.ptr<tensor<144xf32>> |
| %130 = util.global.address @"__iree_flow___sm_node882__m.layer-142.depthwise_kernel" : !util.ptr<tensor<5x5x144x1xf32>> |
| %131 = util.global.address @"__iree_flow___sm_node888__m.layer-143.gamma" : !util.ptr<tensor<144xf32>> |
| %132 = util.global.address @"__iree_flow___sm_node889__m.layer-143.beta" : !util.ptr<tensor<144xf32>> |
| %133 = util.global.address @"__iree_flow___sm_node890__m.layer-143.moving_mean" : !util.ptr<tensor<144xf32>> |
| %134 = util.global.address @"__iree_flow___sm_node891__m.layer-143.moving_variance" : !util.ptr<tensor<144xf32>> |
| %135 = util.global.address @"__iree_flow___sm_node914__m.layer-150.kernel" : !util.ptr<tensor<1x1x144x40xf32>> |
| %136 = util.global.address @"__iree_flow___sm_node915__m.layer-150.bias" : !util.ptr<tensor<40xf32>> |
| %137 = util.global.address @"__iree_flow___sm_node924__m.layer-152.kernel" : !util.ptr<tensor<1x1x40x144xf32>> |
| %138 = util.global.address @"__iree_flow___sm_node925__m.layer-152.bias" : !util.ptr<tensor<144xf32>> |
| %139 = util.global.address @"__iree_flow___sm_node940__m.layer-157.kernel" : !util.ptr<tensor<1x1x144x48xf32>> |
| %140 = util.global.address @"__iree_flow___sm_node946__m.layer-158.gamma" : !util.ptr<tensor<48xf32>> |
| %141 = util.global.address @"__iree_flow___sm_node947__m.layer-158.beta" : !util.ptr<tensor<48xf32>> |
| %142 = util.global.address @"__iree_flow___sm_node948__m.layer-158.moving_mean" : !util.ptr<tensor<48xf32>> |
| %143 = util.global.address @"__iree_flow___sm_node949__m.layer-158.moving_variance" : !util.ptr<tensor<48xf32>> |
| %144 = util.global.address @"__iree_flow___sm_node958__m.layer-160.kernel" : !util.ptr<tensor<1x1x48x288xf32>> |
| %145 = util.global.address @"__iree_flow___sm_node964__m.layer-161.gamma" : !util.ptr<tensor<288xf32>> |
| %146 = util.global.address @"__iree_flow___sm_node965__m.layer-161.beta" : !util.ptr<tensor<288xf32>> |
| %147 = util.global.address @"__iree_flow___sm_node966__m.layer-161.moving_mean" : !util.ptr<tensor<288xf32>> |
| %148 = util.global.address @"__iree_flow___sm_node967__m.layer-161.moving_variance" : !util.ptr<tensor<288xf32>> |
| %149 = util.global.address @"__iree_flow___sm_node986__m.layer-167.depthwise_kernel" : !util.ptr<tensor<5x5x288x1xf32>> |
| %150 = util.global.address @"__iree_flow___sm_node992__m.layer-168.gamma" : !util.ptr<tensor<288xf32>> |
| %151 = util.global.address @"__iree_flow___sm_node993__m.layer-168.beta" : !util.ptr<tensor<288xf32>> |
| %152 = util.global.address @"__iree_flow___sm_node994__m.layer-168.moving_mean" : !util.ptr<tensor<288xf32>> |
| %153 = util.global.address @"__iree_flow___sm_node995__m.layer-168.moving_variance" : !util.ptr<tensor<288xf32>> |
| %154 = util.global.address @"__iree_flow___sm_node1018__m.layer-175.kernel" : !util.ptr<tensor<1x1x288x72xf32>> |
| %155 = util.global.address @"__iree_flow___sm_node1019__m.layer-175.bias" : !util.ptr<tensor<72xf32>> |
| %156 = util.global.address @"__iree_flow___sm_node1028__m.layer-177.kernel" : !util.ptr<tensor<1x1x72x288xf32>> |
| %157 = util.global.address @"__iree_flow___sm_node1029__m.layer-177.bias" : !util.ptr<tensor<288xf32>> |
| %158 = util.global.address @"__iree_flow___sm_node1044__m.layer-182.kernel" : !util.ptr<tensor<1x1x288x96xf32>> |
| %159 = util.global.address @"__iree_flow___sm_node1050__m.layer-183.gamma" : !util.ptr<tensor<96xf32>> |
| %160 = util.global.address @"__iree_flow___sm_node1051__m.layer-183.beta" : !util.ptr<tensor<96xf32>> |
| %161 = util.global.address @"__iree_flow___sm_node1052__m.layer-183.moving_mean" : !util.ptr<tensor<96xf32>> |
| %162 = util.global.address @"__iree_flow___sm_node1053__m.layer-183.moving_variance" : !util.ptr<tensor<96xf32>> |
| %163 = util.global.address @"__iree_flow___sm_node1058__m.layer-184.kernel" : !util.ptr<tensor<1x1x96x576xf32>> |
| %164 = util.global.address @"__iree_flow___sm_node1064__m.layer-185.gamma" : !util.ptr<tensor<576xf32>> |
| %165 = util.global.address @"__iree_flow___sm_node1065__m.layer-185.beta" : !util.ptr<tensor<576xf32>> |
| %166 = util.global.address @"__iree_flow___sm_node1066__m.layer-185.moving_mean" : !util.ptr<tensor<576xf32>> |
| %167 = util.global.address @"__iree_flow___sm_node1067__m.layer-185.moving_variance" : !util.ptr<tensor<576xf32>> |
| %168 = util.global.address @"__iree_flow___sm_node1082__m.layer-190.depthwise_kernel" : !util.ptr<tensor<5x5x576x1xf32>> |
| %169 = util.global.address @"__iree_flow___sm_node1088__m.layer-191.gamma" : !util.ptr<tensor<576xf32>> |
| %170 = util.global.address @"__iree_flow___sm_node1089__m.layer-191.beta" : !util.ptr<tensor<576xf32>> |
| %171 = util.global.address @"__iree_flow___sm_node1090__m.layer-191.moving_mean" : !util.ptr<tensor<576xf32>> |
| %172 = util.global.address @"__iree_flow___sm_node1091__m.layer-191.moving_variance" : !util.ptr<tensor<576xf32>> |
| %173 = util.global.address @"__iree_flow___sm_node1114__m.layer-198.kernel" : !util.ptr<tensor<1x1x576x144xf32>> |
| %174 = util.global.address @"__iree_flow___sm_node1115__m.layer-198.bias" : !util.ptr<tensor<144xf32>> |
| %175 = util.global.address @"__iree_flow___sm_node1124__m.layer-200.kernel" : !util.ptr<tensor<1x1x144x576xf32>> |
| %176 = util.global.address @"__iree_flow___sm_node1125__m.layer-200.bias" : !util.ptr<tensor<576xf32>> |
| %177 = util.global.address @"__iree_flow___sm_node1140__m.layer-205.kernel" : !util.ptr<tensor<1x1x576x96xf32>> |
| %178 = util.global.address @"__iree_flow___sm_node1146__m.layer-206.gamma" : !util.ptr<tensor<96xf32>> |
| %179 = util.global.address @"__iree_flow___sm_node1147__m.layer-206.beta" : !util.ptr<tensor<96xf32>> |
| %180 = util.global.address @"__iree_flow___sm_node1148__m.layer-206.moving_mean" : !util.ptr<tensor<96xf32>> |
| %181 = util.global.address @"__iree_flow___sm_node1149__m.layer-206.moving_variance" : !util.ptr<tensor<96xf32>> |
| %182 = util.global.address @"__iree_flow___sm_node1158__m.layer-208.kernel" : !util.ptr<tensor<1x1x96x576xf32>> |
| %183 = util.global.address @"__iree_flow___sm_node1164__m.layer-209.gamma" : !util.ptr<tensor<576xf32>> |
| %184 = util.global.address @"__iree_flow___sm_node1165__m.layer-209.beta" : !util.ptr<tensor<576xf32>> |
| %185 = util.global.address @"__iree_flow___sm_node1166__m.layer-209.moving_mean" : !util.ptr<tensor<576xf32>> |
| %186 = util.global.address @"__iree_flow___sm_node1167__m.layer-209.moving_variance" : !util.ptr<tensor<576xf32>> |
| %187 = util.global.address @"__iree_flow___sm_node1182__m.layer-214.depthwise_kernel" : !util.ptr<tensor<5x5x576x1xf32>> |
| %188 = util.global.address @"__iree_flow___sm_node1188__m.layer-215.gamma" : !util.ptr<tensor<576xf32>> |
| %189 = util.global.address @"__iree_flow___sm_node1189__m.layer-215.beta" : !util.ptr<tensor<576xf32>> |
| %190 = util.global.address @"__iree_flow___sm_node1190__m.layer-215.moving_mean" : !util.ptr<tensor<576xf32>> |
| %191 = util.global.address @"__iree_flow___sm_node1191__m.layer-215.moving_variance" : !util.ptr<tensor<576xf32>> |
| %192 = util.global.address @"__iree_flow___sm_node1214__m.layer-222.kernel" : !util.ptr<tensor<1x1x576x144xf32>> |
| %193 = util.global.address @"__iree_flow___sm_node1215__m.layer-222.bias" : !util.ptr<tensor<144xf32>> |
| %194 = util.global.address @"__iree_flow___sm_node1224__m.layer-224.kernel" : !util.ptr<tensor<1x1x144x576xf32>> |
| %195 = util.global.address @"__iree_flow___sm_node1225__m.layer-224.bias" : !util.ptr<tensor<576xf32>> |
| %196 = util.global.address @"__iree_flow___sm_node1240__m.layer-229.kernel" : !util.ptr<tensor<1x1x576x96xf32>> |
| %197 = util.global.address @"__iree_flow___sm_node1246__m.layer-230.gamma" : !util.ptr<tensor<96xf32>> |
| %198 = util.global.address @"__iree_flow___sm_node1247__m.layer-230.beta" : !util.ptr<tensor<96xf32>> |
| %199 = util.global.address @"__iree_flow___sm_node1248__m.layer-230.moving_mean" : !util.ptr<tensor<96xf32>> |
| %200 = util.global.address @"__iree_flow___sm_node1249__m.layer-230.moving_variance" : !util.ptr<tensor<96xf32>> |
| %201 = util.global.address @"__iree_flow___sm_node1258__m.layer-232.kernel" : !util.ptr<tensor<1x1x96x576xf32>> |
| %202 = util.global.address @"__iree_flow___sm_node1264__m.layer-233.gamma" : !util.ptr<tensor<576xf32>> |
| %203 = util.global.address @"__iree_flow___sm_node1265__m.layer-233.beta" : !util.ptr<tensor<576xf32>> |
| %204 = util.global.address @"__iree_flow___sm_node1266__m.layer-233.moving_mean" : !util.ptr<tensor<576xf32>> |
| %205 = util.global.address @"__iree_flow___sm_node1267__m.layer-233.moving_variance" : !util.ptr<tensor<576xf32>> |
| %206 = util.global.address @"__iree_flow___sm_node1290__m.layer-240.kernel" : !util.ptr<tensor<1x1x576x1024xf32>> |
| %207 = util.global.address @"__iree_flow___sm_node1291__m.layer-240.bias" : !util.ptr<tensor<1024xf32>> |
| %208 = util.global.address @"__iree_flow___sm_node1310__m.layer-246.kernel" : !util.ptr<tensor<1x1x1024x1000xf32>> |
| %209 = util.global.address @"__iree_flow___sm_node1311__m.layer-246.bias" : !util.ptr<tensor<1000xf32>> |
| %210 = mhlo.constant dense<0.00784313772> : tensor<1x224x224x3xf32> |
| %211 = mhlo.constant dense<-1.000000e+00> : tensor<1x224x224x3xf32> |
| %212 = mhlo.constant dense<3.000000e+00> : tensor<1x112x112x16xf32> |
| %213 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x16xf32> |
| %214 = mhlo.constant dense<3.000000e+00> : tensor<1x28x28x96xf32> |
| %215 = mhlo.constant dense<3.000000e+00> : tensor<1x14x14x96xf32> |
| %216 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x96xf32> |
| %217 = mhlo.constant dense<3.000000e+00> : tensor<1x14x14x240xf32> |
| %218 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x240xf32> |
| %219 = mhlo.constant dense<3.000000e+00> : tensor<1x14x14x120xf32> |
| %220 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x120xf32> |
| %221 = mhlo.constant dense<3.000000e+00> : tensor<1x14x14x144xf32> |
| %222 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x144xf32> |
| %223 = mhlo.constant dense<3.000000e+00> : tensor<1x14x14x288xf32> |
| %224 = mhlo.constant dense<3.000000e+00> : tensor<1x7x7x288xf32> |
| %225 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x288xf32> |
| %226 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x576xf32> |
| %227 = mhlo.constant dense<3.000000e+00> : tensor<1x7x7x576xf32> |
| %228 = mhlo.constant dense<3.000000e+00> : tensor<1x1x1x1024xf32> |
| %229 = mhlo.constant dense<0.166666672> : tensor<1x112x112x16xf32> |
| %230 = mhlo.constant dense<0.166666672> : tensor<1x1x1x16xf32> |
| %231 = mhlo.constant dense<0.166666672> : tensor<1x28x28x96xf32> |
| %232 = mhlo.constant dense<0.166666672> : tensor<1x14x14x96xf32> |
| %233 = mhlo.constant dense<0.166666672> : tensor<1x1x1x96xf32> |
| %234 = mhlo.constant dense<0.166666672> : tensor<1x14x14x240xf32> |
| %235 = mhlo.constant dense<0.166666672> : tensor<1x1x1x240xf32> |
| %236 = mhlo.constant dense<0.166666672> : tensor<1x14x14x120xf32> |
| %237 = mhlo.constant dense<0.166666672> : tensor<1x1x1x120xf32> |
| %238 = mhlo.constant dense<0.166666672> : tensor<1x14x14x144xf32> |
| %239 = mhlo.constant dense<0.166666672> : tensor<1x1x1x144xf32> |
| %240 = mhlo.constant dense<0.166666672> : tensor<1x14x14x288xf32> |
| %241 = mhlo.constant dense<0.166666672> : tensor<1x7x7x288xf32> |
| %242 = mhlo.constant dense<0.166666672> : tensor<1x1x1x288xf32> |
| %243 = mhlo.constant dense<0.166666672> : tensor<1x1x1x576xf32> |
| %244 = mhlo.constant dense<0.166666672> : tensor<1x7x7x576xf32> |
| %245 = mhlo.constant dense<0.166666672> : tensor<1x1x1x1024xf32> |
| %246 = mhlo.constant dense<0.000000e+00> : tensor<1x56x56x16xf32> |
| %247 = mhlo.constant dense<3.136000e+03> : tensor<1x16xf32> |
| %248 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x8xf32> |
| %249 = mhlo.constant dense<0.000000e+00> : tensor<1x56x56x72xf32> |
| %250 = mhlo.constant dense<0.000000e+00> : tensor<1x28x28x72xf32> |
| %251 = mhlo.constant dense<0.000000e+00> : tensor<1x28x28x88xf32> |
| %252 = mhlo.constant dense<1.960000e+02> : tensor<1x96xf32> |
| %253 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x24xf32> |
| %254 = mhlo.constant dense<1.960000e+02> : tensor<1x240xf32> |
| %255 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x64xf32> |
| %256 = mhlo.constant dense<1.960000e+02> : tensor<1x120xf32> |
| %257 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x32xf32> |
| %258 = mhlo.constant dense<1.960000e+02> : tensor<1x144xf32> |
| %259 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x40xf32> |
| %260 = mhlo.constant dense<4.900000e+01> : tensor<1x288xf32> |
| %261 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x72xf32> |
| %262 = mhlo.constant dense<0.000000e+00> : tensor<1x1x1x144xf32> |
| %263 = mhlo.constant dense<4.900000e+01> : tensor<1x576xf32> |
| %264 = mhlo.constant dense<6.000000e+00> : tensor<f32> |
| %265 = mhlo.constant dense<0xFF800000> : tensor<f32> |
| %266 = mhlo.constant dense<0.000000e+00> : tensor<f32> |
| %267 = util.global.load.indirect %205 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %268 = util.global.load.indirect %204 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %269 = util.global.load.indirect %203 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %270 = util.global.load.indirect %202 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %271 = util.global.load.indirect %201 : !util.ptr<tensor<1x1x96x576xf32>> -> tensor<1x1x96x576xf32> |
| %272 = util.global.load.indirect %207 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %273 = util.global.load.indirect %206 : !util.ptr<tensor<1x1x576x1024xf32>> -> tensor<1x1x576x1024xf32> |
| %274 = util.global.load.indirect %4 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %275 = util.global.load.indirect %3 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %276 = util.global.load.indirect %2 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %277 = util.global.load.indirect %1 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %278 = util.global.load.indirect %0 : !util.ptr<tensor<3x3x3x16xf32>> -> tensor<3x3x3x16xf32> |
| %279 = util.global.load.indirect %191 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %280 = util.global.load.indirect %190 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %281 = util.global.load.indirect %189 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %282 = util.global.load.indirect %188 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %283 = util.global.load.indirect %187 : !util.ptr<tensor<5x5x576x1xf32>> -> tensor<5x5x576x1xf32> |
| %284 = util.global.load.indirect %186 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %285 = util.global.load.indirect %185 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %286 = util.global.load.indirect %184 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %287 = util.global.load.indirect %183 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %288 = util.global.load.indirect %182 : !util.ptr<tensor<1x1x96x576xf32>> -> tensor<1x1x96x576xf32> |
| %289 = util.global.load.indirect %200 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %290 = util.global.load.indirect %199 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %291 = util.global.load.indirect %198 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %292 = util.global.load.indirect %197 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %293 = util.global.load.indirect %196 : !util.ptr<tensor<1x1x576x96xf32>> -> tensor<1x1x576x96xf32> |
| %294 = util.global.load.indirect %195 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %295 = util.global.load.indirect %194 : !util.ptr<tensor<1x1x144x576xf32>> -> tensor<1x1x144x576xf32> |
| %296 = util.global.load.indirect %193 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %297 = util.global.load.indirect %192 : !util.ptr<tensor<1x1x576x144xf32>> -> tensor<1x1x576x144xf32> |
| %298 = util.global.load.indirect %28 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %299 = util.global.load.indirect %27 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %300 = util.global.load.indirect %26 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %301 = util.global.load.indirect %25 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %302 = util.global.load.indirect %24 : !util.ptr<tensor<3x3x72x1xf32>> -> tensor<3x3x72x1xf32> |
| %303 = util.global.load.indirect %23 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %304 = util.global.load.indirect %22 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %305 = util.global.load.indirect %21 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %306 = util.global.load.indirect %20 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %307 = util.global.load.indirect %19 : !util.ptr<tensor<1x1x16x72xf32>> -> tensor<1x1x16x72xf32> |
| %308 = util.global.load.indirect %33 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %309 = util.global.load.indirect %32 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %310 = util.global.load.indirect %31 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %311 = util.global.load.indirect %30 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %312 = util.global.load.indirect %29 : !util.ptr<tensor<1x1x72x24xf32>> -> tensor<1x1x72x24xf32> |
| %313 = util.global.load.indirect %43 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %314 = util.global.load.indirect %42 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %315 = util.global.load.indirect %41 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %316 = util.global.load.indirect %40 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %317 = util.global.load.indirect %39 : !util.ptr<tensor<3x3x88x1xf32>> -> tensor<3x3x88x1xf32> |
| %318 = util.global.load.indirect %38 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %319 = util.global.load.indirect %37 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %320 = util.global.load.indirect %36 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %321 = util.global.load.indirect %35 : !util.ptr<tensor<88xf32>> -> tensor<88xf32> |
| %322 = util.global.load.indirect %34 : !util.ptr<tensor<1x1x24x88xf32>> -> tensor<1x1x24x88xf32> |
| %323 = util.global.load.indirect %48 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %324 = util.global.load.indirect %47 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %325 = util.global.load.indirect %46 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %326 = util.global.load.indirect %45 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %327 = util.global.load.indirect %44 : !util.ptr<tensor<1x1x88x24xf32>> -> tensor<1x1x88x24xf32> |
| %328 = util.global.load.indirect %58 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %329 = util.global.load.indirect %57 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %330 = util.global.load.indirect %56 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %331 = util.global.load.indirect %55 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %332 = util.global.load.indirect %54 : !util.ptr<tensor<5x5x96x1xf32>> -> tensor<5x5x96x1xf32> |
| %333 = util.global.load.indirect %53 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %334 = util.global.load.indirect %52 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %335 = util.global.load.indirect %51 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %336 = util.global.load.indirect %50 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %337 = util.global.load.indirect %49 : !util.ptr<tensor<1x1x24x96xf32>> -> tensor<1x1x24x96xf32> |
| %338 = util.global.load.indirect %67 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %339 = util.global.load.indirect %66 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %340 = util.global.load.indirect %65 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %341 = util.global.load.indirect %64 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %342 = util.global.load.indirect %63 : !util.ptr<tensor<1x1x96x40xf32>> -> tensor<1x1x96x40xf32> |
| %343 = util.global.load.indirect %62 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %344 = util.global.load.indirect %61 : !util.ptr<tensor<1x1x24x96xf32>> -> tensor<1x1x24x96xf32> |
| %345 = util.global.load.indirect %60 : !util.ptr<tensor<24xf32>> -> tensor<24xf32> |
| %346 = util.global.load.indirect %59 : !util.ptr<tensor<1x1x96x24xf32>> -> tensor<1x1x96x24xf32> |
| %347 = util.global.load.indirect %77 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %348 = util.global.load.indirect %76 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %349 = util.global.load.indirect %75 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %350 = util.global.load.indirect %74 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %351 = util.global.load.indirect %73 : !util.ptr<tensor<5x5x240x1xf32>> -> tensor<5x5x240x1xf32> |
| %352 = util.global.load.indirect %72 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %353 = util.global.load.indirect %71 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %354 = util.global.load.indirect %70 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %355 = util.global.load.indirect %69 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %356 = util.global.load.indirect %68 : !util.ptr<tensor<1x1x40x240xf32>> -> tensor<1x1x40x240xf32> |
| %357 = util.global.load.indirect %86 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %358 = util.global.load.indirect %85 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %359 = util.global.load.indirect %84 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %360 = util.global.load.indirect %83 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %361 = util.global.load.indirect %82 : !util.ptr<tensor<1x1x240x40xf32>> -> tensor<1x1x240x40xf32> |
| %362 = util.global.load.indirect %81 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %363 = util.global.load.indirect %80 : !util.ptr<tensor<1x1x64x240xf32>> -> tensor<1x1x64x240xf32> |
| %364 = util.global.load.indirect %79 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %365 = util.global.load.indirect %78 : !util.ptr<tensor<1x1x240x64xf32>> -> tensor<1x1x240x64xf32> |
| %366 = util.global.load.indirect %96 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %367 = util.global.load.indirect %95 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %368 = util.global.load.indirect %94 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %369 = util.global.load.indirect %93 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %370 = util.global.load.indirect %92 : !util.ptr<tensor<5x5x240x1xf32>> -> tensor<5x5x240x1xf32> |
| %371 = util.global.load.indirect %91 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %372 = util.global.load.indirect %90 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %373 = util.global.load.indirect %89 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %374 = util.global.load.indirect %88 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %375 = util.global.load.indirect %87 : !util.ptr<tensor<1x1x40x240xf32>> -> tensor<1x1x40x240xf32> |
| %376 = util.global.load.indirect %105 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %377 = util.global.load.indirect %104 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %378 = util.global.load.indirect %103 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %379 = util.global.load.indirect %102 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %380 = util.global.load.indirect %101 : !util.ptr<tensor<1x1x240x40xf32>> -> tensor<1x1x240x40xf32> |
| %381 = util.global.load.indirect %100 : !util.ptr<tensor<240xf32>> -> tensor<240xf32> |
| %382 = util.global.load.indirect %99 : !util.ptr<tensor<1x1x64x240xf32>> -> tensor<1x1x64x240xf32> |
| %383 = util.global.load.indirect %98 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %384 = util.global.load.indirect %97 : !util.ptr<tensor<1x1x240x64xf32>> -> tensor<1x1x240x64xf32> |
| %385 = util.global.load.indirect %115 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %386 = util.global.load.indirect %114 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %387 = util.global.load.indirect %113 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %388 = util.global.load.indirect %112 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %389 = util.global.load.indirect %111 : !util.ptr<tensor<5x5x120x1xf32>> -> tensor<5x5x120x1xf32> |
| %390 = util.global.load.indirect %110 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %391 = util.global.load.indirect %109 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %392 = util.global.load.indirect %108 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %393 = util.global.load.indirect %107 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %394 = util.global.load.indirect %106 : !util.ptr<tensor<1x1x40x120xf32>> -> tensor<1x1x40x120xf32> |
| %395 = util.global.load.indirect %124 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %396 = util.global.load.indirect %123 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %397 = util.global.load.indirect %122 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %398 = util.global.load.indirect %121 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %399 = util.global.load.indirect %120 : !util.ptr<tensor<1x1x120x48xf32>> -> tensor<1x1x120x48xf32> |
| %400 = util.global.load.indirect %119 : !util.ptr<tensor<120xf32>> -> tensor<120xf32> |
| %401 = util.global.load.indirect %118 : !util.ptr<tensor<1x1x32x120xf32>> -> tensor<1x1x32x120xf32> |
| %402 = util.global.load.indirect %117 : !util.ptr<tensor<32xf32>> -> tensor<32xf32> |
| %403 = util.global.load.indirect %116 : !util.ptr<tensor<1x1x120x32xf32>> -> tensor<1x1x120x32xf32> |
| %404 = util.global.load.indirect %134 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %405 = util.global.load.indirect %133 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %406 = util.global.load.indirect %132 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %407 = util.global.load.indirect %131 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %408 = util.global.load.indirect %130 : !util.ptr<tensor<5x5x144x1xf32>> -> tensor<5x5x144x1xf32> |
| %409 = util.global.load.indirect %129 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %410 = util.global.load.indirect %128 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %411 = util.global.load.indirect %127 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %412 = util.global.load.indirect %126 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %413 = util.global.load.indirect %125 : !util.ptr<tensor<1x1x48x144xf32>> -> tensor<1x1x48x144xf32> |
| %414 = util.global.load.indirect %143 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %415 = util.global.load.indirect %142 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %416 = util.global.load.indirect %141 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %417 = util.global.load.indirect %140 : !util.ptr<tensor<48xf32>> -> tensor<48xf32> |
| %418 = util.global.load.indirect %139 : !util.ptr<tensor<1x1x144x48xf32>> -> tensor<1x1x144x48xf32> |
| %419 = util.global.load.indirect %138 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %420 = util.global.load.indirect %137 : !util.ptr<tensor<1x1x40x144xf32>> -> tensor<1x1x40x144xf32> |
| %421 = util.global.load.indirect %136 : !util.ptr<tensor<40xf32>> -> tensor<40xf32> |
| %422 = util.global.load.indirect %135 : !util.ptr<tensor<1x1x144x40xf32>> -> tensor<1x1x144x40xf32> |
| %423 = util.global.load.indirect %153 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %424 = util.global.load.indirect %152 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %425 = util.global.load.indirect %151 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %426 = util.global.load.indirect %150 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %427 = util.global.load.indirect %149 : !util.ptr<tensor<5x5x288x1xf32>> -> tensor<5x5x288x1xf32> |
| %428 = util.global.load.indirect %148 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %429 = util.global.load.indirect %147 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %430 = util.global.load.indirect %146 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %431 = util.global.load.indirect %145 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %432 = util.global.load.indirect %144 : !util.ptr<tensor<1x1x48x288xf32>> -> tensor<1x1x48x288xf32> |
| %433 = util.global.load.indirect %162 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %434 = util.global.load.indirect %161 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %435 = util.global.load.indirect %160 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %436 = util.global.load.indirect %159 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %437 = util.global.load.indirect %158 : !util.ptr<tensor<1x1x288x96xf32>> -> tensor<1x1x288x96xf32> |
| %438 = util.global.load.indirect %157 : !util.ptr<tensor<288xf32>> -> tensor<288xf32> |
| %439 = util.global.load.indirect %156 : !util.ptr<tensor<1x1x72x288xf32>> -> tensor<1x1x72x288xf32> |
| %440 = util.global.load.indirect %155 : !util.ptr<tensor<72xf32>> -> tensor<72xf32> |
| %441 = util.global.load.indirect %154 : !util.ptr<tensor<1x1x288x72xf32>> -> tensor<1x1x288x72xf32> |
| %442 = util.global.load.indirect %172 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %443 = util.global.load.indirect %171 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %444 = util.global.load.indirect %170 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %445 = util.global.load.indirect %169 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %446 = util.global.load.indirect %168 : !util.ptr<tensor<5x5x576x1xf32>> -> tensor<5x5x576x1xf32> |
| %447 = util.global.load.indirect %167 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %448 = util.global.load.indirect %166 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %449 = util.global.load.indirect %165 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %450 = util.global.load.indirect %164 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %451 = util.global.load.indirect %163 : !util.ptr<tensor<1x1x96x576xf32>> -> tensor<1x1x96x576xf32> |
| %452 = util.global.load.indirect %181 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %453 = util.global.load.indirect %180 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %454 = util.global.load.indirect %179 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %455 = util.global.load.indirect %178 : !util.ptr<tensor<96xf32>> -> tensor<96xf32> |
| %456 = util.global.load.indirect %177 : !util.ptr<tensor<1x1x576x96xf32>> -> tensor<1x1x576x96xf32> |
| %457 = util.global.load.indirect %176 : !util.ptr<tensor<576xf32>> -> tensor<576xf32> |
| %458 = util.global.load.indirect %175 : !util.ptr<tensor<1x1x144x576xf32>> -> tensor<1x1x144x576xf32> |
| %459 = util.global.load.indirect %174 : !util.ptr<tensor<144xf32>> -> tensor<144xf32> |
| %460 = util.global.load.indirect %173 : !util.ptr<tensor<1x1x576x144xf32>> -> tensor<1x1x576x144xf32> |
| %461 = util.global.load.indirect %9 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %462 = util.global.load.indirect %8 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %463 = util.global.load.indirect %7 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %464 = util.global.load.indirect %6 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %465 = util.global.load.indirect %5 : !util.ptr<tensor<3x3x16x1xf32>> -> tensor<3x3x16x1xf32> |
| %466 = util.global.load.indirect %18 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %467 = util.global.load.indirect %17 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %468 = util.global.load.indirect %16 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %469 = util.global.load.indirect %15 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %470 = util.global.load.indirect %14 : !util.ptr<tensor<1x1x16x16xf32>> -> tensor<1x1x16x16xf32> |
| %471 = util.global.load.indirect %13 : !util.ptr<tensor<16xf32>> -> tensor<16xf32> |
| %472 = util.global.load.indirect %12 : !util.ptr<tensor<1x1x8x16xf32>> -> tensor<1x1x8x16xf32> |
| %473 = util.global.load.indirect %11 : !util.ptr<tensor<8xf32>> -> tensor<8xf32> |
| %474 = util.global.load.indirect %10 : !util.ptr<tensor<1x1x16x8xf32>> -> tensor<1x1x16x8xf32> |
| %475 = util.global.load.indirect %209 : !util.ptr<tensor<1000xf32>> -> tensor<1000xf32> |
| %476 = util.global.load.indirect %208 : !util.ptr<tensor<1x1x1024x1000xf32>> -> tensor<1x1x1024x1000xf32> |
| %477 = mhlo.multiply %arg0, %210 : tensor<1x224x224x3xf32> |
| %478 = mhlo.add %477, %211 : tensor<1x224x224x3xf32> |
| %479 = "mhlo.convolution"(%478, %278) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<[[0, 1], [0, 1]]> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x224x224x3xf32>, tensor<3x3x3x16xf32>) -> tensor<1x112x112x16xf32> |
| %480 = "mhlo.batch_norm_inference"(%479, %277, %276, %275, %274) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x112x112x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<1x112x112x16xf32> |
| %481 = mhlo.add %480, %212 : tensor<1x112x112x16xf32> |
| %482 = "mhlo.clamp"(%266, %481, %264) : (tensor<f32>, tensor<1x112x112x16xf32>, tensor<f32>) -> tensor<1x112x112x16xf32> |
| %483 = mhlo.multiply %482, %229 : tensor<1x112x112x16xf32> |
| %484 = mhlo.multiply %483, %480 : tensor<1x112x112x16xf32> |
| %485 = "mhlo.pad"(%484, %266) {edge_padding_high = dense<[0, 1, 1, 0]> : tensor<4xi64>, edge_padding_low = dense<0> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x112x112x16xf32>, tensor<f32>) -> tensor<1x113x113x16xf32> |
| %486 = "mhlo.reshape"(%465) : (tensor<3x3x16x1xf32>) -> tensor<3x3x1x16xf32> |
| %487 = "mhlo.convolution"(%485, %486) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 16 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x113x113x16xf32>, tensor<3x3x1x16xf32>) -> tensor<1x56x56x16xf32> |
| %488 = "mhlo.batch_norm_inference"(%487, %464, %463, %462, %461) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x56x56x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<1x56x56x16xf32> |
| %489 = mhlo.maximum %488, %246 : tensor<1x56x56x16xf32> |
| %490 = "mhlo.reduce"(%489, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x56x56x16xf32>, tensor<f32>) -> tensor<1x16xf32> |
| %491 = mhlo.divide %490, %247 : tensor<1x16xf32> |
| %492 = "mhlo.reshape"(%491) : (tensor<1x16xf32>) -> tensor<1x1x1x16xf32> |
| %493 = "mhlo.convolution"(%492, %474) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x16xf32>, tensor<1x1x16x8xf32>) -> tensor<1x1x1x8xf32> |
| %494 = "mhlo.broadcast_in_dim"(%473) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<8xf32>) -> tensor<1x1x1x8xf32> |
| %495 = mhlo.add %493, %494 : tensor<1x1x1x8xf32> |
| %496 = mhlo.maximum %495, %248 : tensor<1x1x1x8xf32> |
| %497 = "mhlo.convolution"(%496, %472) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x8xf32>, tensor<1x1x8x16xf32>) -> tensor<1x1x1x16xf32> |
| %498 = "mhlo.broadcast_in_dim"(%471) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<16xf32>) -> tensor<1x1x1x16xf32> |
| %499 = mhlo.add %497, %498 : tensor<1x1x1x16xf32> |
| %500 = mhlo.add %499, %213 : tensor<1x1x1x16xf32> |
| %501 = "mhlo.clamp"(%266, %500, %264) : (tensor<f32>, tensor<1x1x1x16xf32>, tensor<f32>) -> tensor<1x1x1x16xf32> |
| %502 = mhlo.multiply %501, %230 : tensor<1x1x1x16xf32> |
| %503 = "mhlo.broadcast_in_dim"(%502) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x16xf32>) -> tensor<1x56x56x16xf32> |
| %504 = mhlo.multiply %489, %503 : tensor<1x56x56x16xf32> |
| %505 = "mhlo.convolution"(%504, %470) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x16xf32>, tensor<1x1x16x16xf32>) -> tensor<1x56x56x16xf32> |
| %506 = "mhlo.batch_norm_inference"(%505, %469, %468, %467, %466) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x56x56x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<1x56x56x16xf32> |
| %507 = "mhlo.convolution"(%506, %307) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x16xf32>, tensor<1x1x16x72xf32>) -> tensor<1x56x56x72xf32> |
| %508 = "mhlo.batch_norm_inference"(%507, %306, %305, %304, %303) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x56x56x72xf32>, tensor<72xf32>, tensor<72xf32>, tensor<72xf32>, tensor<72xf32>) -> tensor<1x56x56x72xf32> |
| %509 = mhlo.maximum %508, %249 : tensor<1x56x56x72xf32> |
| %510 = "mhlo.pad"(%509, %266) {edge_padding_high = dense<[0, 1, 1, 0]> : tensor<4xi64>, edge_padding_low = dense<0> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x56x56x72xf32>, tensor<f32>) -> tensor<1x57x57x72xf32> |
| %511 = "mhlo.reshape"(%302) : (tensor<3x3x72x1xf32>) -> tensor<3x3x1x72xf32> |
| %512 = "mhlo.convolution"(%510, %511) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 72 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x57x57x72xf32>, tensor<3x3x1x72xf32>) -> tensor<1x28x28x72xf32> |
| %513 = "mhlo.batch_norm_inference"(%512, %301, %300, %299, %298) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x72xf32>, tensor<72xf32>, tensor<72xf32>, tensor<72xf32>, tensor<72xf32>) -> tensor<1x28x28x72xf32> |
| %514 = mhlo.maximum %513, %250 : tensor<1x28x28x72xf32> |
| %515 = "mhlo.convolution"(%514, %312) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x72xf32>, tensor<1x1x72x24xf32>) -> tensor<1x28x28x24xf32> |
| %516 = "mhlo.batch_norm_inference"(%515, %311, %310, %309, %308) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x24xf32>, tensor<24xf32>, tensor<24xf32>, tensor<24xf32>, tensor<24xf32>) -> tensor<1x28x28x24xf32> |
| %517 = "mhlo.convolution"(%516, %322) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x24xf32>, tensor<1x1x24x88xf32>) -> tensor<1x28x28x88xf32> |
| %518 = "mhlo.batch_norm_inference"(%517, %321, %320, %319, %318) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x88xf32>, tensor<88xf32>, tensor<88xf32>, tensor<88xf32>, tensor<88xf32>) -> tensor<1x28x28x88xf32> |
| %519 = mhlo.maximum %518, %251 : tensor<1x28x28x88xf32> |
| %520 = "mhlo.reshape"(%317) : (tensor<3x3x88x1xf32>) -> tensor<3x3x1x88xf32> |
| %521 = "mhlo.convolution"(%519, %520) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 88 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x88xf32>, tensor<3x3x1x88xf32>) -> tensor<1x28x28x88xf32> |
| %522 = "mhlo.batch_norm_inference"(%521, %316, %315, %314, %313) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x88xf32>, tensor<88xf32>, tensor<88xf32>, tensor<88xf32>, tensor<88xf32>) -> tensor<1x28x28x88xf32> |
| %523 = mhlo.maximum %522, %251 : tensor<1x28x28x88xf32> |
| %524 = "mhlo.convolution"(%523, %327) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x88xf32>, tensor<1x1x88x24xf32>) -> tensor<1x28x28x24xf32> |
| %525 = "mhlo.batch_norm_inference"(%524, %326, %325, %324, %323) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x24xf32>, tensor<24xf32>, tensor<24xf32>, tensor<24xf32>, tensor<24xf32>) -> tensor<1x28x28x24xf32> |
| %526 = mhlo.add %516, %525 : tensor<1x28x28x24xf32> |
| %527 = "mhlo.convolution"(%526, %337) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x24xf32>, tensor<1x1x24x96xf32>) -> tensor<1x28x28x96xf32> |
| %528 = "mhlo.batch_norm_inference"(%527, %336, %335, %334, %333) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x28x28x96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>) -> tensor<1x28x28x96xf32> |
| %529 = mhlo.add %528, %214 : tensor<1x28x28x96xf32> |
| %530 = "mhlo.clamp"(%266, %529, %264) : (tensor<f32>, tensor<1x28x28x96xf32>, tensor<f32>) -> tensor<1x28x28x96xf32> |
| %531 = mhlo.multiply %530, %231 : tensor<1x28x28x96xf32> |
| %532 = mhlo.multiply %531, %528 : tensor<1x28x28x96xf32> |
| %533 = "mhlo.pad"(%532, %266) {edge_padding_high = dense<[0, 2, 2, 0]> : tensor<4xi64>, edge_padding_low = dense<[0, 1, 1, 0]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x28x28x96xf32>, tensor<f32>) -> tensor<1x31x31x96xf32> |
| %534 = "mhlo.reshape"(%332) : (tensor<5x5x96x1xf32>) -> tensor<5x5x1x96xf32> |
| %535 = "mhlo.convolution"(%533, %534) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 96 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x31x31x96xf32>, tensor<5x5x1x96xf32>) -> tensor<1x14x14x96xf32> |
| %536 = "mhlo.batch_norm_inference"(%535, %331, %330, %329, %328) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>) -> tensor<1x14x14x96xf32> |
| %537 = mhlo.add %536, %215 : tensor<1x14x14x96xf32> |
| %538 = "mhlo.clamp"(%266, %537, %264) : (tensor<f32>, tensor<1x14x14x96xf32>, tensor<f32>) -> tensor<1x14x14x96xf32> |
| %539 = mhlo.multiply %538, %232 : tensor<1x14x14x96xf32> |
| %540 = mhlo.multiply %539, %536 : tensor<1x14x14x96xf32> |
| %541 = "mhlo.reduce"(%540, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x14x14x96xf32>, tensor<f32>) -> tensor<1x96xf32> |
| %542 = mhlo.divide %541, %252 : tensor<1x96xf32> |
| %543 = "mhlo.reshape"(%542) : (tensor<1x96xf32>) -> tensor<1x1x1x96xf32> |
| %544 = "mhlo.convolution"(%543, %346) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x96xf32>, tensor<1x1x96x24xf32>) -> tensor<1x1x1x24xf32> |
| %545 = "mhlo.broadcast_in_dim"(%345) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<24xf32>) -> tensor<1x1x1x24xf32> |
| %546 = mhlo.add %544, %545 : tensor<1x1x1x24xf32> |
| %547 = mhlo.maximum %546, %253 : tensor<1x1x1x24xf32> |
| %548 = "mhlo.convolution"(%547, %344) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x24xf32>, tensor<1x1x24x96xf32>) -> tensor<1x1x1x96xf32> |
| %549 = "mhlo.broadcast_in_dim"(%343) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<96xf32>) -> tensor<1x1x1x96xf32> |
| %550 = mhlo.add %548, %549 : tensor<1x1x1x96xf32> |
| %551 = mhlo.add %550, %216 : tensor<1x1x1x96xf32> |
| %552 = "mhlo.clamp"(%266, %551, %264) : (tensor<f32>, tensor<1x1x1x96xf32>, tensor<f32>) -> tensor<1x1x1x96xf32> |
| %553 = mhlo.multiply %552, %233 : tensor<1x1x1x96xf32> |
| %554 = "mhlo.broadcast_in_dim"(%553) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x96xf32>) -> tensor<1x14x14x96xf32> |
| %555 = mhlo.multiply %540, %554 : tensor<1x14x14x96xf32> |
| %556 = "mhlo.convolution"(%555, %342) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x96xf32>, tensor<1x1x96x40xf32>) -> tensor<1x14x14x40xf32> |
| %557 = "mhlo.batch_norm_inference"(%556, %341, %340, %339, %338) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>) -> tensor<1x14x14x40xf32> |
| %558 = "mhlo.convolution"(%557, %356) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x40xf32>, tensor<1x1x40x240xf32>) -> tensor<1x14x14x240xf32> |
| %559 = "mhlo.batch_norm_inference"(%558, %355, %354, %353, %352) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>) -> tensor<1x14x14x240xf32> |
| %560 = mhlo.add %559, %217 : tensor<1x14x14x240xf32> |
| %561 = "mhlo.clamp"(%266, %560, %264) : (tensor<f32>, tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x14x14x240xf32> |
| %562 = mhlo.multiply %561, %234 : tensor<1x14x14x240xf32> |
| %563 = mhlo.multiply %562, %559 : tensor<1x14x14x240xf32> |
| %564 = "mhlo.reshape"(%351) : (tensor<5x5x240x1xf32>) -> tensor<5x5x1x240xf32> |
| %565 = "mhlo.convolution"(%563, %564) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 240 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<5x5x1x240xf32>) -> tensor<1x14x14x240xf32> |
| %566 = "mhlo.batch_norm_inference"(%565, %350, %349, %348, %347) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>) -> tensor<1x14x14x240xf32> |
| %567 = mhlo.add %566, %217 : tensor<1x14x14x240xf32> |
| %568 = "mhlo.clamp"(%266, %567, %264) : (tensor<f32>, tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x14x14x240xf32> |
| %569 = mhlo.multiply %568, %234 : tensor<1x14x14x240xf32> |
| %570 = mhlo.multiply %569, %566 : tensor<1x14x14x240xf32> |
| %571 = "mhlo.reduce"(%570, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x240xf32> |
| %572 = mhlo.divide %571, %254 : tensor<1x240xf32> |
| %573 = "mhlo.reshape"(%572) : (tensor<1x240xf32>) -> tensor<1x1x1x240xf32> |
| %574 = "mhlo.convolution"(%573, %365) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x240xf32>, tensor<1x1x240x64xf32>) -> tensor<1x1x1x64xf32> |
| %575 = "mhlo.broadcast_in_dim"(%364) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x1x1x64xf32> |
| %576 = mhlo.add %574, %575 : tensor<1x1x1x64xf32> |
| %577 = mhlo.maximum %576, %255 : tensor<1x1x1x64xf32> |
| %578 = "mhlo.convolution"(%577, %363) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x64xf32>, tensor<1x1x64x240xf32>) -> tensor<1x1x1x240xf32> |
| %579 = "mhlo.broadcast_in_dim"(%362) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<240xf32>) -> tensor<1x1x1x240xf32> |
| %580 = mhlo.add %578, %579 : tensor<1x1x1x240xf32> |
| %581 = mhlo.add %580, %218 : tensor<1x1x1x240xf32> |
| %582 = "mhlo.clamp"(%266, %581, %264) : (tensor<f32>, tensor<1x1x1x240xf32>, tensor<f32>) -> tensor<1x1x1x240xf32> |
| %583 = mhlo.multiply %582, %235 : tensor<1x1x1x240xf32> |
| %584 = "mhlo.broadcast_in_dim"(%583) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x240xf32>) -> tensor<1x14x14x240xf32> |
| %585 = mhlo.multiply %570, %584 : tensor<1x14x14x240xf32> |
| %586 = "mhlo.convolution"(%585, %361) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<1x1x240x40xf32>) -> tensor<1x14x14x40xf32> |
| %587 = "mhlo.batch_norm_inference"(%586, %360, %359, %358, %357) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>) -> tensor<1x14x14x40xf32> |
| %588 = mhlo.add %557, %587 : tensor<1x14x14x40xf32> |
| %589 = "mhlo.convolution"(%588, %375) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x40xf32>, tensor<1x1x40x240xf32>) -> tensor<1x14x14x240xf32> |
| %590 = "mhlo.batch_norm_inference"(%589, %374, %373, %372, %371) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>) -> tensor<1x14x14x240xf32> |
| %591 = mhlo.add %590, %217 : tensor<1x14x14x240xf32> |
| %592 = "mhlo.clamp"(%266, %591, %264) : (tensor<f32>, tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x14x14x240xf32> |
| %593 = mhlo.multiply %592, %234 : tensor<1x14x14x240xf32> |
| %594 = mhlo.multiply %593, %590 : tensor<1x14x14x240xf32> |
| %595 = "mhlo.reshape"(%370) : (tensor<5x5x240x1xf32>) -> tensor<5x5x1x240xf32> |
| %596 = "mhlo.convolution"(%594, %595) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 240 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<5x5x1x240xf32>) -> tensor<1x14x14x240xf32> |
| %597 = "mhlo.batch_norm_inference"(%596, %369, %368, %367, %366) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>, tensor<240xf32>) -> tensor<1x14x14x240xf32> |
| %598 = mhlo.add %597, %217 : tensor<1x14x14x240xf32> |
| %599 = "mhlo.clamp"(%266, %598, %264) : (tensor<f32>, tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x14x14x240xf32> |
| %600 = mhlo.multiply %599, %234 : tensor<1x14x14x240xf32> |
| %601 = mhlo.multiply %600, %597 : tensor<1x14x14x240xf32> |
| %602 = "mhlo.reduce"(%601, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<f32>) -> tensor<1x240xf32> |
| %603 = mhlo.divide %602, %254 : tensor<1x240xf32> |
| %604 = "mhlo.reshape"(%603) : (tensor<1x240xf32>) -> tensor<1x1x1x240xf32> |
| %605 = "mhlo.convolution"(%604, %384) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x240xf32>, tensor<1x1x240x64xf32>) -> tensor<1x1x1x64xf32> |
| %606 = "mhlo.broadcast_in_dim"(%383) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x1x1x64xf32> |
| %607 = mhlo.add %605, %606 : tensor<1x1x1x64xf32> |
| %608 = mhlo.maximum %607, %255 : tensor<1x1x1x64xf32> |
| %609 = "mhlo.convolution"(%608, %382) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x64xf32>, tensor<1x1x64x240xf32>) -> tensor<1x1x1x240xf32> |
| %610 = "mhlo.broadcast_in_dim"(%381) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<240xf32>) -> tensor<1x1x1x240xf32> |
| %611 = mhlo.add %609, %610 : tensor<1x1x1x240xf32> |
| %612 = mhlo.add %611, %218 : tensor<1x1x1x240xf32> |
| %613 = "mhlo.clamp"(%266, %612, %264) : (tensor<f32>, tensor<1x1x1x240xf32>, tensor<f32>) -> tensor<1x1x1x240xf32> |
| %614 = mhlo.multiply %613, %235 : tensor<1x1x1x240xf32> |
| %615 = "mhlo.broadcast_in_dim"(%614) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x240xf32>) -> tensor<1x14x14x240xf32> |
| %616 = mhlo.multiply %601, %615 : tensor<1x14x14x240xf32> |
| %617 = "mhlo.convolution"(%616, %380) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x240xf32>, tensor<1x1x240x40xf32>) -> tensor<1x14x14x40xf32> |
| %618 = "mhlo.batch_norm_inference"(%617, %379, %378, %377, %376) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>, tensor<40xf32>) -> tensor<1x14x14x40xf32> |
| %619 = mhlo.add %588, %618 : tensor<1x14x14x40xf32> |
| %620 = "mhlo.convolution"(%619, %394) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x40xf32>, tensor<1x1x40x120xf32>) -> tensor<1x14x14x120xf32> |
| %621 = "mhlo.batch_norm_inference"(%620, %393, %392, %391, %390) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x120xf32>, tensor<120xf32>, tensor<120xf32>, tensor<120xf32>, tensor<120xf32>) -> tensor<1x14x14x120xf32> |
| %622 = mhlo.add %621, %219 : tensor<1x14x14x120xf32> |
| %623 = "mhlo.clamp"(%266, %622, %264) : (tensor<f32>, tensor<1x14x14x120xf32>, tensor<f32>) -> tensor<1x14x14x120xf32> |
| %624 = mhlo.multiply %623, %236 : tensor<1x14x14x120xf32> |
| %625 = mhlo.multiply %624, %621 : tensor<1x14x14x120xf32> |
| %626 = "mhlo.reshape"(%389) : (tensor<5x5x120x1xf32>) -> tensor<5x5x1x120xf32> |
| %627 = "mhlo.convolution"(%625, %626) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 120 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x120xf32>, tensor<5x5x1x120xf32>) -> tensor<1x14x14x120xf32> |
| %628 = "mhlo.batch_norm_inference"(%627, %388, %387, %386, %385) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x120xf32>, tensor<120xf32>, tensor<120xf32>, tensor<120xf32>, tensor<120xf32>) -> tensor<1x14x14x120xf32> |
| %629 = mhlo.add %628, %219 : tensor<1x14x14x120xf32> |
| %630 = "mhlo.clamp"(%266, %629, %264) : (tensor<f32>, tensor<1x14x14x120xf32>, tensor<f32>) -> tensor<1x14x14x120xf32> |
| %631 = mhlo.multiply %630, %236 : tensor<1x14x14x120xf32> |
| %632 = mhlo.multiply %631, %628 : tensor<1x14x14x120xf32> |
| %633 = "mhlo.reduce"(%632, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x14x14x120xf32>, tensor<f32>) -> tensor<1x120xf32> |
| %634 = mhlo.divide %633, %256 : tensor<1x120xf32> |
| %635 = "mhlo.reshape"(%634) : (tensor<1x120xf32>) -> tensor<1x1x1x120xf32> |
| %636 = "mhlo.convolution"(%635, %403) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x120xf32>, tensor<1x1x120x32xf32>) -> tensor<1x1x1x32xf32> |
| %637 = "mhlo.broadcast_in_dim"(%402) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<32xf32>) -> tensor<1x1x1x32xf32> |
| %638 = mhlo.add %636, %637 : tensor<1x1x1x32xf32> |
| %639 = mhlo.maximum %638, %257 : tensor<1x1x1x32xf32> |
| %640 = "mhlo.convolution"(%639, %401) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x32xf32>, tensor<1x1x32x120xf32>) -> tensor<1x1x1x120xf32> |
| %641 = "mhlo.broadcast_in_dim"(%400) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<120xf32>) -> tensor<1x1x1x120xf32> |
| %642 = mhlo.add %640, %641 : tensor<1x1x1x120xf32> |
| %643 = mhlo.add %642, %220 : tensor<1x1x1x120xf32> |
| %644 = "mhlo.clamp"(%266, %643, %264) : (tensor<f32>, tensor<1x1x1x120xf32>, tensor<f32>) -> tensor<1x1x1x120xf32> |
| %645 = mhlo.multiply %644, %237 : tensor<1x1x1x120xf32> |
| %646 = "mhlo.broadcast_in_dim"(%645) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x120xf32>) -> tensor<1x14x14x120xf32> |
| %647 = mhlo.multiply %632, %646 : tensor<1x14x14x120xf32> |
| %648 = "mhlo.convolution"(%647, %399) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x120xf32>, tensor<1x1x120x48xf32>) -> tensor<1x14x14x48xf32> |
| %649 = "mhlo.batch_norm_inference"(%648, %398, %397, %396, %395) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x48xf32>, tensor<48xf32>, tensor<48xf32>, tensor<48xf32>, tensor<48xf32>) -> tensor<1x14x14x48xf32> |
| %650 = "mhlo.convolution"(%649, %413) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x48xf32>, tensor<1x1x48x144xf32>) -> tensor<1x14x14x144xf32> |
| %651 = "mhlo.batch_norm_inference"(%650, %412, %411, %410, %409) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x144xf32>, tensor<144xf32>, tensor<144xf32>, tensor<144xf32>, tensor<144xf32>) -> tensor<1x14x14x144xf32> |
| %652 = mhlo.add %651, %221 : tensor<1x14x14x144xf32> |
| %653 = "mhlo.clamp"(%266, %652, %264) : (tensor<f32>, tensor<1x14x14x144xf32>, tensor<f32>) -> tensor<1x14x14x144xf32> |
| %654 = mhlo.multiply %653, %238 : tensor<1x14x14x144xf32> |
| %655 = mhlo.multiply %654, %651 : tensor<1x14x14x144xf32> |
| %656 = "mhlo.reshape"(%408) : (tensor<5x5x144x1xf32>) -> tensor<5x5x1x144xf32> |
| %657 = "mhlo.convolution"(%655, %656) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 144 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x144xf32>, tensor<5x5x1x144xf32>) -> tensor<1x14x14x144xf32> |
| %658 = "mhlo.batch_norm_inference"(%657, %407, %406, %405, %404) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x144xf32>, tensor<144xf32>, tensor<144xf32>, tensor<144xf32>, tensor<144xf32>) -> tensor<1x14x14x144xf32> |
| %659 = mhlo.add %658, %221 : tensor<1x14x14x144xf32> |
| %660 = "mhlo.clamp"(%266, %659, %264) : (tensor<f32>, tensor<1x14x14x144xf32>, tensor<f32>) -> tensor<1x14x14x144xf32> |
| %661 = mhlo.multiply %660, %238 : tensor<1x14x14x144xf32> |
| %662 = mhlo.multiply %661, %658 : tensor<1x14x14x144xf32> |
| %663 = "mhlo.reduce"(%662, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x14x14x144xf32>, tensor<f32>) -> tensor<1x144xf32> |
| %664 = mhlo.divide %663, %258 : tensor<1x144xf32> |
| %665 = "mhlo.reshape"(%664) : (tensor<1x144xf32>) -> tensor<1x1x1x144xf32> |
| %666 = "mhlo.convolution"(%665, %422) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x144xf32>, tensor<1x1x144x40xf32>) -> tensor<1x1x1x40xf32> |
| %667 = "mhlo.broadcast_in_dim"(%421) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<40xf32>) -> tensor<1x1x1x40xf32> |
| %668 = mhlo.add %666, %667 : tensor<1x1x1x40xf32> |
| %669 = mhlo.maximum %668, %259 : tensor<1x1x1x40xf32> |
| %670 = "mhlo.convolution"(%669, %420) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x40xf32>, tensor<1x1x40x144xf32>) -> tensor<1x1x1x144xf32> |
| %671 = "mhlo.broadcast_in_dim"(%419) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<144xf32>) -> tensor<1x1x1x144xf32> |
| %672 = mhlo.add %670, %671 : tensor<1x1x1x144xf32> |
| %673 = mhlo.add %672, %222 : tensor<1x1x1x144xf32> |
| %674 = "mhlo.clamp"(%266, %673, %264) : (tensor<f32>, tensor<1x1x1x144xf32>, tensor<f32>) -> tensor<1x1x1x144xf32> |
| %675 = mhlo.multiply %674, %239 : tensor<1x1x1x144xf32> |
| %676 = "mhlo.broadcast_in_dim"(%675) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x144xf32>) -> tensor<1x14x14x144xf32> |
| %677 = mhlo.multiply %662, %676 : tensor<1x14x14x144xf32> |
| %678 = "mhlo.convolution"(%677, %418) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x144xf32>, tensor<1x1x144x48xf32>) -> tensor<1x14x14x48xf32> |
| %679 = "mhlo.batch_norm_inference"(%678, %417, %416, %415, %414) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x48xf32>, tensor<48xf32>, tensor<48xf32>, tensor<48xf32>, tensor<48xf32>) -> tensor<1x14x14x48xf32> |
| %680 = mhlo.add %649, %679 : tensor<1x14x14x48xf32> |
| %681 = "mhlo.convolution"(%680, %432) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x48xf32>, tensor<1x1x48x288xf32>) -> tensor<1x14x14x288xf32> |
| %682 = "mhlo.batch_norm_inference"(%681, %431, %430, %429, %428) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x14x14x288xf32>, tensor<288xf32>, tensor<288xf32>, tensor<288xf32>, tensor<288xf32>) -> tensor<1x14x14x288xf32> |
| %683 = mhlo.add %682, %223 : tensor<1x14x14x288xf32> |
| %684 = "mhlo.clamp"(%266, %683, %264) : (tensor<f32>, tensor<1x14x14x288xf32>, tensor<f32>) -> tensor<1x14x14x288xf32> |
| %685 = mhlo.multiply %684, %240 : tensor<1x14x14x288xf32> |
| %686 = mhlo.multiply %685, %682 : tensor<1x14x14x288xf32> |
| %687 = "mhlo.pad"(%686, %266) {edge_padding_high = dense<[0, 2, 2, 0]> : tensor<4xi64>, edge_padding_low = dense<[0, 1, 1, 0]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x14x14x288xf32>, tensor<f32>) -> tensor<1x17x17x288xf32> |
| %688 = "mhlo.reshape"(%427) : (tensor<5x5x288x1xf32>) -> tensor<5x5x1x288xf32> |
| %689 = "mhlo.convolution"(%687, %688) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 288 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x17x17x288xf32>, tensor<5x5x1x288xf32>) -> tensor<1x7x7x288xf32> |
| %690 = "mhlo.batch_norm_inference"(%689, %426, %425, %424, %423) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x288xf32>, tensor<288xf32>, tensor<288xf32>, tensor<288xf32>, tensor<288xf32>) -> tensor<1x7x7x288xf32> |
| %691 = mhlo.add %690, %224 : tensor<1x7x7x288xf32> |
| %692 = "mhlo.clamp"(%266, %691, %264) : (tensor<f32>, tensor<1x7x7x288xf32>, tensor<f32>) -> tensor<1x7x7x288xf32> |
| %693 = mhlo.multiply %692, %241 : tensor<1x7x7x288xf32> |
| %694 = mhlo.multiply %693, %690 : tensor<1x7x7x288xf32> |
| %695 = "mhlo.reduce"(%694, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x7x7x288xf32>, tensor<f32>) -> tensor<1x288xf32> |
| %696 = mhlo.divide %695, %260 : tensor<1x288xf32> |
| %697 = "mhlo.reshape"(%696) : (tensor<1x288xf32>) -> tensor<1x1x1x288xf32> |
| %698 = "mhlo.convolution"(%697, %441) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x288xf32>, tensor<1x1x288x72xf32>) -> tensor<1x1x1x72xf32> |
| %699 = "mhlo.broadcast_in_dim"(%440) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<72xf32>) -> tensor<1x1x1x72xf32> |
| %700 = mhlo.add %698, %699 : tensor<1x1x1x72xf32> |
| %701 = mhlo.maximum %700, %261 : tensor<1x1x1x72xf32> |
| %702 = "mhlo.convolution"(%701, %439) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x72xf32>, tensor<1x1x72x288xf32>) -> tensor<1x1x1x288xf32> |
| %703 = "mhlo.broadcast_in_dim"(%438) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<288xf32>) -> tensor<1x1x1x288xf32> |
| %704 = mhlo.add %702, %703 : tensor<1x1x1x288xf32> |
| %705 = mhlo.add %704, %225 : tensor<1x1x1x288xf32> |
| %706 = "mhlo.clamp"(%266, %705, %264) : (tensor<f32>, tensor<1x1x1x288xf32>, tensor<f32>) -> tensor<1x1x1x288xf32> |
| %707 = mhlo.multiply %706, %242 : tensor<1x1x1x288xf32> |
| %708 = "mhlo.broadcast_in_dim"(%707) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x288xf32>) -> tensor<1x7x7x288xf32> |
| %709 = mhlo.multiply %694, %708 : tensor<1x7x7x288xf32> |
| %710 = "mhlo.convolution"(%709, %437) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x288xf32>, tensor<1x1x288x96xf32>) -> tensor<1x7x7x96xf32> |
| %711 = "mhlo.batch_norm_inference"(%710, %436, %435, %434, %433) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>) -> tensor<1x7x7x96xf32> |
| %712 = "mhlo.convolution"(%711, %451) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x96xf32>, tensor<1x1x96x576xf32>) -> tensor<1x7x7x576xf32> |
| %713 = "mhlo.batch_norm_inference"(%712, %450, %449, %448, %447) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>) -> tensor<1x7x7x576xf32> |
| %714 = mhlo.add %713, %227 : tensor<1x7x7x576xf32> |
| %715 = "mhlo.clamp"(%266, %714, %264) : (tensor<f32>, tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x7x7x576xf32> |
| %716 = mhlo.multiply %715, %244 : tensor<1x7x7x576xf32> |
| %717 = mhlo.multiply %716, %713 : tensor<1x7x7x576xf32> |
| %718 = "mhlo.reshape"(%446) : (tensor<5x5x576x1xf32>) -> tensor<5x5x1x576xf32> |
| %719 = "mhlo.convolution"(%717, %718) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 576 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<5x5x1x576xf32>) -> tensor<1x7x7x576xf32> |
| %720 = "mhlo.batch_norm_inference"(%719, %445, %444, %443, %442) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>) -> tensor<1x7x7x576xf32> |
| %721 = mhlo.add %720, %227 : tensor<1x7x7x576xf32> |
| %722 = "mhlo.clamp"(%266, %721, %264) : (tensor<f32>, tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x7x7x576xf32> |
| %723 = mhlo.multiply %722, %244 : tensor<1x7x7x576xf32> |
| %724 = mhlo.multiply %723, %720 : tensor<1x7x7x576xf32> |
| %725 = "mhlo.reduce"(%724, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x576xf32> |
| %726 = mhlo.divide %725, %263 : tensor<1x576xf32> |
| %727 = "mhlo.reshape"(%726) : (tensor<1x576xf32>) -> tensor<1x1x1x576xf32> |
| %728 = "mhlo.convolution"(%727, %460) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x576xf32>, tensor<1x1x576x144xf32>) -> tensor<1x1x1x144xf32> |
| %729 = "mhlo.broadcast_in_dim"(%459) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<144xf32>) -> tensor<1x1x1x144xf32> |
| %730 = mhlo.add %728, %729 : tensor<1x1x1x144xf32> |
| %731 = mhlo.maximum %730, %262 : tensor<1x1x1x144xf32> |
| %732 = "mhlo.convolution"(%731, %458) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x144xf32>, tensor<1x1x144x576xf32>) -> tensor<1x1x1x576xf32> |
| %733 = "mhlo.broadcast_in_dim"(%457) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<576xf32>) -> tensor<1x1x1x576xf32> |
| %734 = mhlo.add %732, %733 : tensor<1x1x1x576xf32> |
| %735 = mhlo.add %734, %226 : tensor<1x1x1x576xf32> |
| %736 = "mhlo.clamp"(%266, %735, %264) : (tensor<f32>, tensor<1x1x1x576xf32>, tensor<f32>) -> tensor<1x1x1x576xf32> |
| %737 = mhlo.multiply %736, %243 : tensor<1x1x1x576xf32> |
| %738 = "mhlo.broadcast_in_dim"(%737) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x576xf32>) -> tensor<1x7x7x576xf32> |
| %739 = mhlo.multiply %724, %738 : tensor<1x7x7x576xf32> |
| %740 = "mhlo.convolution"(%739, %456) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<1x1x576x96xf32>) -> tensor<1x7x7x96xf32> |
| %741 = "mhlo.batch_norm_inference"(%740, %455, %454, %453, %452) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>) -> tensor<1x7x7x96xf32> |
| %742 = mhlo.add %711, %741 : tensor<1x7x7x96xf32> |
| %743 = "mhlo.convolution"(%742, %288) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x96xf32>, tensor<1x1x96x576xf32>) -> tensor<1x7x7x576xf32> |
| %744 = "mhlo.batch_norm_inference"(%743, %287, %286, %285, %284) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>) -> tensor<1x7x7x576xf32> |
| %745 = mhlo.add %744, %227 : tensor<1x7x7x576xf32> |
| %746 = "mhlo.clamp"(%266, %745, %264) : (tensor<f32>, tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x7x7x576xf32> |
| %747 = mhlo.multiply %746, %244 : tensor<1x7x7x576xf32> |
| %748 = mhlo.multiply %747, %744 : tensor<1x7x7x576xf32> |
| %749 = "mhlo.reshape"(%283) : (tensor<5x5x576x1xf32>) -> tensor<5x5x1x576xf32> |
| %750 = "mhlo.convolution"(%748, %749) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 576 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<5x5x1x576xf32>) -> tensor<1x7x7x576xf32> |
| %751 = "mhlo.batch_norm_inference"(%750, %282, %281, %280, %279) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>) -> tensor<1x7x7x576xf32> |
| %752 = mhlo.add %751, %227 : tensor<1x7x7x576xf32> |
| %753 = "mhlo.clamp"(%266, %752, %264) : (tensor<f32>, tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x7x7x576xf32> |
| %754 = mhlo.multiply %753, %244 : tensor<1x7x7x576xf32> |
| %755 = mhlo.multiply %754, %751 : tensor<1x7x7x576xf32> |
| %756 = "mhlo.reduce"(%755, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x576xf32> |
| %757 = mhlo.divide %756, %263 : tensor<1x576xf32> |
| %758 = "mhlo.reshape"(%757) : (tensor<1x576xf32>) -> tensor<1x1x1x576xf32> |
| %759 = "mhlo.convolution"(%758, %297) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x576xf32>, tensor<1x1x576x144xf32>) -> tensor<1x1x1x144xf32> |
| %760 = "mhlo.broadcast_in_dim"(%296) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<144xf32>) -> tensor<1x1x1x144xf32> |
| %761 = mhlo.add %759, %760 : tensor<1x1x1x144xf32> |
| %762 = mhlo.maximum %761, %262 : tensor<1x1x1x144xf32> |
| %763 = "mhlo.convolution"(%762, %295) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x144xf32>, tensor<1x1x144x576xf32>) -> tensor<1x1x1x576xf32> |
| %764 = "mhlo.broadcast_in_dim"(%294) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<576xf32>) -> tensor<1x1x1x576xf32> |
| %765 = mhlo.add %763, %764 : tensor<1x1x1x576xf32> |
| %766 = mhlo.add %765, %226 : tensor<1x1x1x576xf32> |
| %767 = "mhlo.clamp"(%266, %766, %264) : (tensor<f32>, tensor<1x1x1x576xf32>, tensor<f32>) -> tensor<1x1x1x576xf32> |
| %768 = mhlo.multiply %767, %243 : tensor<1x1x1x576xf32> |
| %769 = "mhlo.broadcast_in_dim"(%768) {broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>} : (tensor<1x1x1x576xf32>) -> tensor<1x7x7x576xf32> |
| %770 = mhlo.multiply %755, %769 : tensor<1x7x7x576xf32> |
| %771 = "mhlo.convolution"(%770, %293) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<1x1x576x96xf32>) -> tensor<1x7x7x96xf32> |
| %772 = "mhlo.batch_norm_inference"(%771, %292, %291, %290, %289) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>, tensor<96xf32>) -> tensor<1x7x7x96xf32> |
| %773 = mhlo.add %742, %772 : tensor<1x7x7x96xf32> |
| %774 = "mhlo.convolution"(%773, %271) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x96xf32>, tensor<1x1x96x576xf32>) -> tensor<1x7x7x576xf32> |
| %775 = "mhlo.batch_norm_inference"(%774, %270, %269, %268, %267) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64} : (tensor<1x7x7x576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>, tensor<576xf32>) -> tensor<1x7x7x576xf32> |
| %776 = mhlo.add %775, %227 : tensor<1x7x7x576xf32> |
| %777 = "mhlo.clamp"(%266, %776, %264) : (tensor<f32>, tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x7x7x576xf32> |
| %778 = mhlo.multiply %777, %244 : tensor<1x7x7x576xf32> |
| %779 = mhlo.multiply %778, %775 : tensor<1x7x7x576xf32> |
| %780 = "mhlo.reduce"(%779, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x7x7x576xf32>, tensor<f32>) -> tensor<1x576xf32> |
| %781 = mhlo.divide %780, %263 : tensor<1x576xf32> |
| %782 = "mhlo.reshape"(%781) : (tensor<1x576xf32>) -> tensor<1x1x1x576xf32> |
| %783 = "mhlo.convolution"(%782, %273) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x576xf32>, tensor<1x1x576x1024xf32>) -> tensor<1x1x1x1024xf32> |
| %784 = "mhlo.broadcast_in_dim"(%272) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x1x1x1024xf32> |
| %785 = mhlo.add %783, %784 : tensor<1x1x1x1024xf32> |
| %786 = mhlo.add %785, %228 : tensor<1x1x1x1024xf32> |
| %787 = "mhlo.clamp"(%266, %786, %264) : (tensor<f32>, tensor<1x1x1x1024xf32>, tensor<f32>) -> tensor<1x1x1x1024xf32> |
| %788 = mhlo.multiply %787, %245 : tensor<1x1x1x1024xf32> |
| %789 = mhlo.multiply %788, %785 : tensor<1x1x1x1024xf32> |
| %790 = "mhlo.convolution"(%789, %476) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x1x1024xf32>, tensor<1x1x1024x1000xf32>) -> tensor<1x1x1x1000xf32> |
| %791 = "mhlo.broadcast_in_dim"(%475) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1000xf32>) -> tensor<1x1x1x1000xf32> |
| %792 = mhlo.add %790, %791 : tensor<1x1x1x1000xf32> |
| %793 = "mhlo.reshape"(%792) : (tensor<1x1x1x1000xf32>) -> tensor<1x1000xf32> |
| %794 = "mhlo.reduce"(%793, %265) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.maximum %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x1000xf32>, tensor<f32>) -> tensor<1xf32> |
| %795 = "mhlo.broadcast_in_dim"(%794) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x1000xf32> |
| %796 = mhlo.subtract %793, %795 : tensor<1x1000xf32> |
| %797 = "mhlo.exponential"(%796) : (tensor<1x1000xf32>) -> tensor<1x1000xf32> |
| %798 = "mhlo.reduce"(%797, %266) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %801 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%801) : (tensor<f32>) -> () |
| }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x1000xf32>, tensor<f32>) -> tensor<1xf32> |
| %799 = "mhlo.broadcast_in_dim"(%798) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x1000xf32> |
| %800 = mhlo.divide %797, %799 : tensor<1x1000xf32> |
| check.expect_almost_eq_const(%800, dense<0.001> : tensor<1x1000xf32>) : tensor<1x1000xf32> |
| return |
| } |
| } |
| |
