TF_Input feed_port;
  EXPECT_EQ(1, TF_OperationOutputConsumers(TF_Output{feed, 0}, &feed_port, 1));
  EXPECT_EQ(add, feed_port.oper);
  EXPECT_EQ(0, feed_port.index);

  // The scalar const oper also has a consumer.
  ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{three, 0}));
  TF_Input three_port;
  EXPECT_EQ(1,

  return lib_handle;
}

TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; }

void TF_DeleteLibraryHandle(TF_Library* lib_handle) {
  if (lib_handle == nullptr) return;
  tensorflow::port::Free(const_cast<void*>(lib_handle->op_list.data));
  delete lib_handle;
}

TF_Buffer* TF_GetAllOpList() {
  std::vector<tensorflow::OpDef> op_defs;
  tensorflow::OpRegistry::Global()->GetRegisteredOps(&op_defs);

    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_buffer_internal",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:platform_port",
        "//tensorflow/core/platform:protobuf",
        "//tensorflow/core/platform:status",
    ],
)

tf_cuda_library(
    name = "tf_buffer_internal",
    hdrs = [
        "tf_buffer.h",

  tensorflow::string value = cell->cell.value();
  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };
}

TFE_MonitoringStringGauge0* TFE_MonitoringNewStringGauge0(

  job_def->set_name(job_name);
  for (int i = 0; i < num_tasks; i++) {
    int port = tensorflow::testing::PickUnusedPortOrDie();
    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost:", port)});
    LOG(INFO) << "Picked test port: " << port << " for job: " << job_name
              << ", task: " << i;
  }
  return server_def;
}

        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",
        "//tensorflow/lite/toco:toco_graphviz_dump_options",
        "//tensorflow/lite/toco:toco_port",
        "//tensorflow/lite/toco:toco_tooling",
        "//tensorflow/lite/toco:tooling_util",
        "//tensorflow/lite/toco:types_proto_cc",
        "//tensorflow/lite:framework",

        requires all optimizer variables to be created at the first
        `apply_gradients()` or `minimize()` call. If your workflow calls
        optimizer to update different parts of model in multiple stages,
        please call `optimizer.build(model.trainable_variables)` before the
        training loop.
    *   **Performance regression on `ParameterServerStrategy`.** This could be

  if (!status->status.ok()) {
    return 0;
  }
  return op_reg_data->op_def.is_stateful();
}

void TF_InitMain(const char* usage, int* argc, char*** argv) {
  tensorflow::port::InitMain(usage, argc, argv);
}

int TF_PickUnusedPortOrDie() {
  return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,