"//tensorflow/cc/experimental/libtf:__pkg__",
        "//tensorflow/cc/experimental/libtf:__subpackages__",
        # copybara:uncomment_begin(google-only)
        # "//tensorflow/cc/experimental/tf2:__pkg__",
        # "//tensorflow/cc/experimental/tf2:__subpackages__",
        # copybara:uncomment_end
        "//tensorflow/compiler/mlir/tensorflow/c:__subpackages__",
        "//tensorflow/core/transforms:__subpackages__",
    ],

limitations under the License.
==============================================================================*/

// This pass separates SparseCore, TensorCore, and non-TPU operations into
// separate functions for proper sequencing of TF2 TPU Embedding (see
// tpu_embedding_v3.py). This pass is a precursor for pipelining (see
// embedding_pipelining.cc) and DOES NOT permit parallel execution across SC and

  return *jitrt_flags;
}

ConfigProto::Experimental::MlirBridgeRollout GetMlirBridgeRolloutState(
    std::optional<const ConfigProto> config_proto) {
  // TF1 graphs that do not override Sessions's ConfigProto and TF2 graphs
  // can enable/disable the graph via tf_mlir_enable_mlir_bridge.
  auto tf_mlir_enable_mlir_bridge =
      GetMlirCommonFlags()->tf_mlir_enable_mlir_bridge;
  if (tf_mlir_enable_mlir_bridge !=

                          status.ToString())
      .IgnoreError();

  return status;
}

// V1 Compat Bridge takes a TF Executor dialect and extracts the TF2 portion
// and inserts it into a submodule. We just want to run the clustering
// portion of the pipeline on just the single submodule.
absl::Status RunClusteringPipelineOnSubmodule(

    representative_dataset: rd.RepresentativeDataset,
) -> None:
  """Runs the representative dataset through a function for calibration.

  NOTE: This is intended to be run in eager mode (TF2).

  Args:
    func: The function to run the representative samples through.
    representative_dataset: Representative dataset used for calibration. The

        "//tensorflow/core:tensorflow",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core:testlib",
    ],
)

# A subset of the TF2 saved models can be generated with this tool.
py_strict_binary(
    name = "testdata/generate_saved_models",
    srcs = ["testdata/generate_saved_models.py"],
    data = [
        ":saved_model_asset_data",

# RESULT1: name: "Identity"
# RESULT1-NEXT: quantization:
# RESULT1-NEXT: scale: [ 0.007523 ],
# RESULT1-NEXT: zero_point: [ 116 ]

# TODO  Actually RESULT1 represents in incomplete implementation
# Currently TF2.2.0-rc3 all but the first fake_quant in 
# sequence are dropped.  A correct transalation would retain the second as a requantization
# op.

# CORRECT1:name: "Identity"
# CORRECT1-NEXT: quantization:

    extra result of type `!tf_executor.control` is always produced by every
    `tf_executor.island`.
    Within an island, execution semantics follow standard sequential behavior as
    expected by TF2 and by compiler analyses and transformations, and values
    can’t be dead. Other nested `tf_executor.graph` operations can be present in
    the region to re-enable the TensorFlow executor for a subsection of the
    code.