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;

  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();

struct TF_VariableInfo {
  TF_VariableInfo() = delete;
  // TF_VariableInfo is constructed here by TensorFlow, and will be passed to
  // plugin as a opaque pointer. Plugin will need to call C APIs below to
  // operate on TF_VariableInfo (such as allocate temp tensor for the `var` held
  // by the underlying tensorflow::VariableInfo.
  TF_VariableInfo(int index, const std::string& name, tensorflow::Var* var) {

       ++component_index) {
    // TODO(allenl): Add a C API for summarizing tensors. Currently custom
    // devices limiting themselves to a C API (for ABI compatibility) would need
    // to implement summarization for component tensors themselves.
    ImmediateExecutionTensorHandle* component =
        tensorflow::unwrap(tensors_[component_index].get());
    std::string component_summary;

   */
  // 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.

 private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

    return nullptr;
  }
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  // TODO(apassos) do we need to wrap the deallocator here to make sure to sync
  // the device?
  TF_ManagedBuffer* buf =
      new TF_ManagedBuffer(data, len, deallocator, deallocator_arg,
                           /*owns_memory=*/false);