/tensorflow/python/autograph/ @mdanatg
/tensorflow/python/debug @caisq
/tensorflow/python/eager @rohan100jain
/tensorflow/tools/docs/ @markdaoust
/tensorflow/compiler/mlir/ @aminim
/tensorflow/core/ir/ @aminim
/tensorflow/core/transforms/ @aminim

iler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow...

// Plugin should implement TF_InitGraph to register graph optimizers.
void TF_InitGraph(TP_OptimizerRegistrationParams* params, TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed
// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `num_values` and `storage_size`, they
// will be used in `TF_GetNodesToPreserveList`.

                                               void* device_info);

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will

        # "//tensorflow/cc/experimental/tf2:__subpackages__",
        # copybara:uncomment_end
        "//tensorflow/compiler/mlir/tensorflow/c:__subpackages__",
        "//tensorflow/core/transforms:__subpackages__",
    ],
    deps = [
        ":c_api_macros_hdrs",
        "@local_tsl//tsl/platform:status",
        "@local_xla//xla/tsl/c:tsl_status",
        "@local_xla//xla/tsl/c:tsl_status_internal",

*   `Optimizer.__init__` now accepts a `gradient_aggregator` to allow for
    customization of how gradients are aggregated across devices, as well as
    `gradients_transformers` to allow for custom gradient transformations (such
    as gradient clipping).
*   Improvements to Keras preprocessing layers:
    *   TextVectorization can now accept a vocabulary list or file as an init
        arg.

        "//tensorflow/lite:stateful_error_reporter",
        "//tensorflow/lite:string_util",
        "//tensorflow/lite:util",
        "//tensorflow/python/grappler:cost_analyzer_lib",
        "//tensorflow/tools/graph_transforms:transform_graph_lib",
    ] + select({
        "//tensorflow/compiler/mlir/python:disable_mlir_config": [],
        "//conditions:default": [
            "//tensorflow/compiler/mlir/python:mlir",
        ],