bool HasTensor(const string& name) const;
  const string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.

  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;
  std::vector<const char*> missing_unused_key_names;
  std::vector<int> missing_unused_key_indexes;

  // Backing memory for missing_unused_key_names values.
  std::vector<tensorflow::string> missing_unused_key_names_data;
};

struct TF_DeviceList {
  std::vector<tensorflow::DeviceAttributes> response;
};

struct TF_Function {

  std::vector<char*> translated_names;
  translated_names.reserve(files.size());
  for (int i = 0; i < files.size(); i++)
    translated_names.push_back(strdup(TranslateName(files[i]).c_str()));

  bool result;
  if (status == nullptr) {
    result = ops_->paths_exist(filesystem_.get(), translated_names.data(),
                               files.size(), nullptr);
  } else {

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),

  // Check unused input mappings
  int num_unused_input_mappings;
  const char** src_names;
  int* src_indexes;
  TF_ImportGraphDefResultsMissingUnusedInputMappings(
      results, &num_unused_input_mappings, &src_names, &src_indexes);
  ASSERT_EQ(1, num_unused_input_mappings);
  EXPECT_EQ(string("fake"), string(src_names[0]));
  EXPECT_EQ(0, src_indexes[0]);

  TF_DeleteImportGraphDefResults(results);

        *   Using `functools.wraps` on a function with different signature
        *   Using `functools.partial` with an invalid `tf.function` input
    *   `tf.function` now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.

// Plugin should implement TF_InitGraph to register graph optimizers.
void TF_InitGraph(TP_OptimizerRegistrationParams* params, TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed
// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `num_values` and `storage_size`, they

  std::vector<string> input_names(ninputs);
  std::vector<string> output_names(noutputs);
  std::vector<string> target_oper_names(ntargets);
  for (int i = 0; i < ninputs; ++i) {
    input_names[i] = c_input_names[i];
  }
  for (int i = 0; i < noutputs; ++i) {
    output_names[i] = c_output_names[i];
  }
  for (int i = 0; i < ntargets; ++i) {
    target_oper_names[i] = c_target_oper_names[i];
  }

    data = [
        ":test_op1.so",
        "//tensorflow/cc/saved_model:saved_model_half_plus_two",
    ],
    linkopts = select({
        "//tensorflow:macos": ["-headerpad_max_install_names"],
        "//conditions:default": [],
    }),
    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],