int src_index, TF_Output dst) {
  opts->tensor_id_data.push_back(src_name);
  const string& src_name_str = opts->tensor_id_data.back();
  // We don't need to store dst's name in tensor_id_data, since `dst` must
  // outlive the ImportGraphDef call.
  opts->opts.input_map[TensorId(src_name_str, src_index)] = ToTensorId(dst);
}

  EXPECT_EQ(1, TF_OperationOutputListLength(feed, "output", s));
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));

  tensorflow::AttrValue attr_value;

namespace tf_gcs_filesystem {

bool RamFileBlockCache::BlockNotStale(const std::shared_ptr<Block>& block) {
  absl::MutexLock l(&block->mu);
  if (block->state != FetchState::FINISHED) {
    return true;  // No need to check for staleness.
  }
  if (max_staleness_ == 0) return true;  // Not enforcing staleness.
  return timer_seconds_() - block->timestamp <= max_staleness_;
}

//
// Note that to test the local filesystem we use an empty value.

namespace tensorflow {
namespace {

using ::tensorflow::error::Code;

// As we need to test multiple URI schemes we need a parameterized test.
// Furthermore, since each test creates and deletes files, we will use the same
// fixture to create new directories in `SetUp`. Each directory will reside in

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  std::string translated_name = TranslateName(dir);
  // Note that `children` is allocated by the plugin and freed by core
  // TensorFlow, so we need to use `plugin_memory_free_` here.
  char** children = nullptr;
  const int num_children =
      ops_->get_children(filesystem_.get(), translated_name.c_str(), &children,
                         plugin_status.get());

      TF_FinalizeFunction(graph_ctx, add_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  // Note: TF_OutputList does not own the underlying AbstractTensors, those
  // need to be deleted explicitly.
  TF_DeleteAbstractTensor(TF_OutputListGet(add_outputs, 0));

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  TF_Operation* op;
  Split3Helper(input, graph, s, name, &op);
  return op;
}

bool IsPlaceholder(const tensorflow::NodeDef& node_def) {
  if (node_def.op() != "Placeholder" || node_def.name() != "feed") {
    return false;
  }
  bool found_dtype = false;
  bool found_shape = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {

   */
  // Define
  TF_Operation* feed = Placeholder(func_graph_, s_);
  Define(-1, {}, {feed}, {feed}, {});

  // Use, run, and verify
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, 3);
  VerifyFDef(empty_, {{"feed_0", DT_INT32}}, {{"feed", DT_INT32}},
             {{"feed_0", "feed"}}, {});
}

    // If this file does not exist on server, we will need to sync it.
    bool sync_need = (status_code == TF_NOT_FOUND);
    file->plugin_file = new tf_writable_file::GCSFile(
        {std::move(bucket), std::move(object), &gcs_file->gcs_client,
         TempFile(temp_file_name, std::ios::binary | std::ios::app), sync_need,
         -1});
  } else {
    // If compose is true, we do not download anything.

                                                           TF_DeleteStatus);

  TF_Operation* feed = Placeholder(func_graph.get(), s.get());
  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,