#include "tensorflow/c/ops.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/registration/registration.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"

static void merge_summary_shape_inference_fn(TF_ShapeInferenceContext* ctx,
                                             TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");

#include "tensorflow/c/experimental/saved_model/internal/saved_model_api_type.h"
#include "tensorflow/c/experimental/saved_model/internal/signature_def_function_type.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/common_runtime/eager/context.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"

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

#include "tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h"

#include <cstring>

#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/blocking_counter.h"
#include "tensorflow/core/platform/cloud/now_seconds_env.h"

#include "mlir/Support/FileUtilities.h"  // from @llvm-project
#include "stablehlo/dialect/Register.h"  // from @stablehlo
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

  int64_t (*tell)(const TF_WritableFile* file, TF_Status* status);

  /// Flushes `*file` and syncs contents to filesystem.
  ///
  /// This call might not block, and when it returns the contents might not have
  /// been fully persisted.
  ///
  /// DEFAULT IMPLEMENTATION: No op.
  void (*flush)(const TF_WritableFile* file, TF_Status* status);

  /// Syncs contents of `*file` with the filesystem.
  ///

#include "tensorflow/c/ops.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/registration/registration.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"

static void scalar_summary_shape_inference_fn(TF_ShapeInferenceContext* ctx,
                                              TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");

    tasks {
      key: 0
      value: "tpuserver:0"
    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();
  TF_Buffer* result = TFE_GetServerDef(expected_text_proto.c_str(), status);
  EXPECT_EQ(TF_GetCode(status), TF_OK);

  ServerDef actual;

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.

void TF_FunctionToFunctionDef(TF_Function* func, TF_Buffer* output_func_def,
                              TF_Status* status) {
  status->status = MessageToBuffer(func->record->fdef(), output_func_def);
}

TF_Function* TF_FunctionImportFunctionDef(const void* proto, size_t proto_len,
                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);

#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(