)]}'
{
  "commit": "5bc8ac237971a71c1e4ad0e8ab50a10d57a92d84",
  "tree": "1f8215622e48084a82d498046004d2e038bc2def",
  "parents": [
    "942f315def49da3a76c05076216e623142b4dfae"
  ],
  "author": {
    "name": "Tzu-Wei Sung",
    "email": "windqaq@gmail.com",
    "time": "Wed Feb 10 00:42:44 2021 -0800"
  },
  "committer": {
    "name": "Tzu-Wei Sung",
    "email": "windqaq@gmail.com",
    "time": "Wed Feb 10 00:42:44 2021 -0800"
  },
  "message": "Partially infer conv return types\n\naf9ad9d introduces conv inferReturnTypes, but skips the inference when either input or filter does not have static shape (unranked or not all dimensions are static).\nThis PR instead tries to infer as many dimensions as possible, which avoids shape information loss when there are only some dynamic dimensions (like batch dim).\n",
  "tree_diff": [
    {
      "type": "modify",
      "old_id": "6a0ab9a67fab738b4bcd884b9d6187f68835bacc",
      "old_mode": 33188,
      "old_path": "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc",
      "new_id": "8e7da245f6d9ea66ce3a606f97a0d6cef2734560",
      "new_mode": 33188,
      "new_path": "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc"
    },
    {
      "type": "modify",
      "old_id": "e226da5970385e5730cc95cb102a767f8fd87d87",
      "old_mode": 33188,
      "old_path": "tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir",
      "new_id": "329f80ed9d2e89d06631bc759b195f7dd15be16e",
      "new_mode": 33188,
      "new_path": "tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir"
    }
  ]
}