export_dir, tag_set, &bundle);
  if (!status->status.ok()) return nullptr;

  // Create a TF_Graph from the MetaGraphDef. This is safe as long as Session
  // extends using GraphDefs. The Graph instance is different, but equivalent
  // to the one used to create the session.
  //
  // TODO(jhseu): When Session is modified to take Graphs instead of

      tf_posix_filesystem::NewAppendableFile;
  ops->filesystem_ops->new_read_only_memory_region_from_file =
      tf_posix_filesystem::NewReadOnlyMemoryRegionFromFile;
  ops->filesystem_ops->create_dir = tf_posix_filesystem::CreateDir;
  ops->filesystem_ops->delete_file = tf_posix_filesystem::DeleteFile;
  ops->filesystem_ops->delete_dir = tf_posix_filesystem::DeleteDir;

      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,

      container_name, plugin_resource_name, &tf_plugin_resource,
      [plugin_resource_name, create_func, create_func_args,
       delete_func](tensorflow::PluginResource** new_resource) {
        void* opaque_plugin_resource = create_func(create_func_args);
        *new_resource = new tensorflow::PluginResource(
            opaque_plugin_resource, plugin_resource_name, delete_func);

  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server2)

      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(

TFE_MonitoringSampler0* TFE_MonitoringNewSampler0(
    const char* name, TFE_MonitoringBuckets* buckets, TF_Status* status,
    const char* description) {
  auto* result = new TFE_MonitoringSampler0(
      {name, buckets->create_buckets(), description});
  tsl::Set_TF_Status_from_Status(status, result->sampler->GetStatus());
  if (!result->sampler->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

  return th;
}

TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);

  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
            std::initializer_list<TF_Output> outputs,
            const std::vector<std::vector<int32_t>>& expected_results) {
    // Create a session for this graph
    CSession csession(host_graph_, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Run
    csession.SetInputs(inputs);
    csession.SetOutputs(outputs);

    TF_SetStatus(status, TF_ALREADY_EXISTS, path);
}

// TODO(vnvo2409): `RecursivelyCreateDir` should use `CreateDir` instead of the
// default implementation. Because we could create an empty object whose
// key is equal to the `path` and Google Cloud Console will automatically
// display it as a directory tree.

void DeleteFile(const TF_Filesystem* filesystem, const char* path,