#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
        update_docs_called(false) {
  }

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
  tensorflow::ApiDefMap api_def_map TF_GUARDED_BY(lock);
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
  bool update_docs_called TF_GUARDED_BY(lock);
  tensorflow::mutex lock;
};

                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
            std::unique_ptr<TensorSliceReader::VarToDataTypeMap> >
  BuildV2VarMaps();

  //
  // For example some tensor handles may represent distributed values, in which
  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.

    string key(v2_reader_->key());
    (*var_to_shape_map)[key] = TensorShape(entry.shape());
    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

  out.resize(block_count, 0);
  // The cache has space for `block_count` blocks. The loop with i = 0 should
  // fill the cache, and the loop with i = 1 should be all cache hits. The
  // fetcher checks that it is called once and only once for each offset (to
  // fetch the corresponding block).
  for (int i = 0; i < 2; i++) {
    for (int j = 0; j < block_count; j++) {

          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};
  bool deallocator_called = false;
  TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
                              &Deallocator, &deallocator_called);
  EXPECT_FALSE(deallocator_called);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
  EXPECT_EQ(2, TF_NumDims(t));
  EXPECT_EQ(dims[0], TF_Dim(t, 0));

                                            int64_t* undeleted_dirs) {
  if (undeleted_files == nullptr || undeleted_dirs == nullptr)
    return errors::FailedPrecondition(
        "DeleteRecursively must not be called with `undeleted_files` or "
        "`undeleted_dirs` set to NULL");

  if (ops_->delete_recursively == nullptr)
    return FileSystem::DeleteRecursively(dirname, token, undeleted_files,

static std::vector<std::string>* SchemeVector() {
  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

    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_DeleteExecutor(executor);
  }
  // Following works in async mode since TFE_ContextAsyncClearError was called.
  TF_SetStatus(status, TF_OK, "");
  if (retvals[0] != nullptr) {
    TFE_DeleteTensorHandle(retvals[0]);
  }
  retvals[0] = nullptr;
  TFE_Execute(matmul2, &retvals[0], &num_retvals, status);

// The returned string is heap-allocated, and caller should call free() on it.
TF_CAPI_EXPORT extern const char* TF_GraphDebugString(TF_Graph* graph,
                                                      size_t* len);

// Returns the function content in a human-readable format, with length set in
// `len`. The format is subject to change in the future.
// The returned string is heap-allocated, and caller should call free() on it.
//