//   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;
  ExecutorPtr executor_ TF_GUARDED_BY(execution_mutex_);

  TF_RETURN_IF_ERROR(ValidatePluginMemoryRoutines(info));

  // Validate and register all filesystems
  // Try to register as many filesystems as possible.
  // Free memory once we no longer need it
  Status status;
  for (int i = 0; i < info->num_schemes; i++) {
    status.Update(ValidateAndRegisterFilesystems(info, i));
    info->plugin_memory_free(info->ops[i].scheme);

                             params->body_graph, s_, "add1");
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    TF_Operation* one = ScalarConst(1, params->body_graph, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    TF_Operation* add2 = Add(add1, one, params->body_graph, s_, "add2");
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    params->body_outputs[0] = {add2, 0};

constexpr char kMaxCacheSize[] = "GCS_READ_CACHE_MAX_SIZE_MB";
constexpr size_t kDefaultMaxCacheSize = 0;
// The environment variable that overrides the maximum staleness of cached file
// contents. Once any block of a file reaches this staleness, all cached blocks
// will be evicted on the next read.
constexpr char kMaxStaleness[] = "GCS_READ_CACHE_MAX_STALENESS";
constexpr uint64_t kDefaultMaxStaleness = 0;

  static std::vector<std::string>* schemes = new std::vector<std::string>;
  return schemes;
}

// `INSTANTIATE_TEST_SUITE_P` is called once for every `TEST_P`. However, we
// only want to analyze the user provided schemes and those that are registered
// only once. Hence, this function keeping another static pointer to a vector
// which contains only the schemes under test.
//

    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =

  TF_Graph* graph = TF_NewGraph();

  // Make two scalar constants.
  TF_Operation* one = ScalarConst(1, graph, s, "one");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* two = ScalarConst(2, graph, s, "two");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Add oper.
  TF_Operation* add = Add(one, two, graph, s, "add");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TFE_DeleteContext(ctx);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  const int num_devices = TF_DeviceListCount(devices);
  EXPECT_GE(num_devices, 1) << "At least one CPU device should exist";
  for (int i = 0; i < num_devices; ++i) {
    EXPECT_NE("", TF_DeviceListName(devices, i, status)) << i;
    EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  }