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{
  "commit": "b3cd60a12f8f1c593a900b61d407eff138c443e5",
  "tree": "3ed2dd5a7e2f39fc401c7e393d9bfee04e8b1ea5",
  "parents": [
    "f782069ecde3774553c0c15e1b953fea40b4a7c2"
  ],
  "author": {
    "name": "Scott Todd",
    "email": "scotttodd@google.com",
    "time": "Thu Oct 12 09:40:30 2023 -0700"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Thu Oct 12 09:40:30 2023 -0700"
  },
  "message": "Add pytorch_jit sample Colab notebook using SHARK-Turbine.  (#15146)\n\nProgress on https://github.com/openxla/iree/issues/15117\r\n\r\nThis notebook shows how to use\r\n[SHARK-Turbine](https://github.com/nod-ai/SHARK-Turbine) for eager\r\nexecution within a PyTorch session using IREE and\r\n[torch-mlir](https://github.com/llvm/torch-mlir) under the covers. I\u0027m\r\nstarting simple to get the concepts across, with minimal API usage and a\r\ntiny `nn.Module` sourced from\r\nhttps://pytorch.org/docs/stable/notes/modules.html. My expectation is\r\nthat this notebook will evolve alongside other notebooks (e.g.\r\n`pytorch_aot.ipynb`), documentation\r\n(https://github.com/openxla/iree/issues/15114), and the SHARK-Turbine\r\nproject itself.\r\n\r\nPreview URL for review:\r\nhttps://colab.research.google.com/github/openxla/iree/blob/scotttodd-pytorch-samples-1/samples/colab/pytorch_jit.ipynb\r\n\r\nskip-ci: no-op",
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