":c_api_internal",
        ":c_api_macros_hdrs",
        ":checkpoint_reader",
        ":tf_buffer",
        ":tf_buffer_internal",
        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_internal",
        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/compiler/jit:flags",

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {

#include "tensorflow/c/experimental/gradients/nn_grad.h"

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/immediate_execution_context.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"

setattr(_current_module, "losses", _losses)
setattr(_current_module, "metrics", _metrics)
setattr(_current_module, "optimizers", _optimizers)
setattr(_current_module, "initializers", _initializers)


# Do an eager load for Keras' code so that any function/method that needs to
# happen at load time will trigger, eg registration of optimizers in the
# SavedModel registry.
# See b/196254385 for more details.
try:

==============================================================================*/
#include "tensorflow/c/experimental/gradients/array_grad.h"

#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"

[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After
the change has been submitted internally, your pull request will be merged

            "//tensorflow/core/common_runtime/eager:attr_builder",
            "//tensorflow/core/common_runtime/eager:context",
            "//tensorflow/core/common_runtime/eager:copy_to_device_node",
            "//tensorflow/core/common_runtime/eager:eager_executor",
            "//tensorflow/core/common_runtime/eager:eager_operation",
            "//tensorflow/core/common_runtime/eager:execute",

        # cc_shared_library to work.
        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:c_api_internal",
        "//tensorflow/c/eager:dlpack",
        "//tensorflow/c/eager:tape",
        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/c/experimental/gradients",

*   Unified eager and `tf.function` execution:

    *   Eager mode can now execute each op as a `tf.function`, allowing for more
        consistent feature support in future releases.
    *   It is available for immediate use.
        *   See the `TF_RUN_EAGER_OP_AS_FUNCTION` environment variable in

  TF_CheckpointReader* reader = new TF_CheckpointReader(filename, status);
  if (!status->status.ok()) {
    TF_DeleteCheckpointReader(reader);
    return nullptr;
  }
  const auto& m = reader->GetVariableToDataTypeMap();
  for (auto it = m.begin(); it != m.end(); ++it)
    reader->variable_list.push_back(it->first);
  std::sort(reader->variable_list.begin(), reader->variable_list.end());
  return reader;
}