//
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will
  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,

  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(/*enable=*/true));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_SILENT);
  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

#include "tensorflow/core/common_runtime/eager/custom_device_op_handler.h"
#include "tensorflow/core/common_runtime/eager/execute.h"
#include "tensorflow/core/common_runtime/eager/placement_utils.h"
#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/attr_value.pb.h"

  opts->session_options.options.config.set_isolate_session_state(
      isolate_session_state);
  TFE_ContextOptionsSetAsync(opts, static_cast<unsigned char>(false));
  TFE_ContextOptionsSetDevicePlacementPolicy(opts, TFE_DEVICE_PLACEMENT_SILENT);
  TFE_Context* ctx = TFE_NewContext(opts, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_ContextSetServerDefWithTimeout(ctx, 0, serialized_server_def.data(),

            "//tensorflow/core/common_runtime/eager:eager_executor",
            "//tensorflow/core/common_runtime/eager:execute",
            "//tensorflow/core/common_runtime/eager:placement_utils",
            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",

        enable changing the underlying storage there.
    *   The following Python symbols were accidentally added in earlier versions
        of TensorFlow and now are removed. Each symbol has a replacement that
        should be used instead, but note the replacement's argument names are
        different.
        *   `tf.quantize_and_dequantize_v4` (accidentally introduced in
            TensorFlow 2.4): Use `tf.quantization.quantize_and_dequantize_v2`