==============================================================================*/

/******************************************************************************
This pass implements automated pipelining for TPU embeddings defined using
the TF2 Embedding API. This is designed for applications that have an
embedding lookup on the SparseCore, followed by one or more dense layers on
TensorCores, optionally followed by a backward pass (training update) with

          * Replace `tf.keras.mixed_precision.experimental.get_layer_policy(layer)` with `layer.dtype_policy`.
* `tf.mixed_precision.experimental.LossScale` and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removed `tf.keras.mixed_precision.experimental` API. The symbols are still available under `tf.compat.v1.mixed_precision`.

  TF_RETURN_IF_ERROR(importer.ConvertDeferredFunctions());

  if (!saved_model->meta_graph_def().has_object_graph_def()) {
    return errors::InvalidArgument(
        "SavedModel does not have an object graph. Please use TF2.");
  }
  auto& object_graph = saved_model->meta_graph_def().object_graph_def();
  ObjectNames object_names(object_graph, exported_names);

  // Clean up a couple func's that always seem to be present when importing a