", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.
  return absl::OkStatus();
}

  }

  {
    // Try to extract the wrong number of components from a parallel tensor
    std::array<TFE_TensorHandle*, 1> correct_components{value_one.get()};
    TensorHandlePtr combined_value = CreatePerDeviceValues(
        context.get(), correct_components, device_name, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

class GCSFilesystemTest : public ::testing::Test {
 public:
  void SetUp() override {
    root_dir_ = io::JoinPath(
        *GetTmpDir(),
        ::testing::UnitTest::GetInstance()->current_test_info()->name());
    status_ = TF_NewStatus();
    filesystem_ = new TF_Filesystem;
    filesystem_->plugin_filesystem = nullptr;
    // Because different tests requires different setup for filesystem. We

    return bytes_transferred;
  };
  for (size_t block_size = 2; block_size <= 4; block_size++) {
    // Make a cache of N-byte block size (1 block) and verify that reads of
    // varying offsets and lengths return correct data.
    tf_gcs_filesystem::RamFileBlockCache cache(block_size, block_size, 0,
                                               fetcher);
    for (size_t offset = 0; offset < 10; offset++) {

  cache1.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 1);
  EXPECT_EQ(num_compute_calls, 2);

  // Define a new cache with max_age > 0 and verify correct behavior.
  tf_gcs_filesystem::ExpiringLRUCache<int> cache2(2, 4);
  num_compute_calls = 0;
  value = -1;

  // Read our first value
  cache2.LookupOrCompute("a", &value, compute_func, &status);

    lru_list_.push_front(key);
    block->lru_iterator = lru_list_.begin();
  }

  // Check for inconsistent state. If there is a block later in the same file
  // in the cache, and our current block is not block size, this likely means
  // we have inconsistent state within the cache. Note: it's possible some
  // incomplete reads may still go undetected.
  if (block->data.size() < block_size_) {

  // current test name.
  //
  // Since the test name contains `/` (due to parameters), this function
  // replaces `/` with `_`.
  //
  // We trade in one extra initialization for readability.
  ModularFileSystemTest() {
    const std::string test_name = tensorflow::str_util::StringReplace(
        ::testing::UnitTest::GetInstance()->current_test_info()->name(), "/",

    const char* msg = "Matrix size-incompatible: In[0]: [2,2], In[1]: [3,2]";
    EXPECT_TRUE(strstr(TF_Message(status), msg) != nullptr)
        << TF_Message(status);
    // Since error is not cleared, the following copy with correct device will
    // still fail.
    TF_SetStatus(status, TF_OK, "");
    TFE_DeleteTensorHandle(retvals[0]);
    TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);

  bool UseFunction() const { return std::get<2>(GetParam()); }
};

TEST_P(CppGradients, TestIdentityNGrad) {
  // This test is interesting because the current implementation of GradientTape
  // would return [0, 1] whereas we use build_default_zeros_grads=false here
  // so we get back [nullptr, 1].

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;

// Copies a function table from plugin memory space to core memory space.
//
// This has three benefits:
//   * allows having newer plugins than the current core TensorFlow: the
//     additional entries in the plugin's table are just discarded;
//   * allows having older plugins than the current core TensorFlow (though
//     we are still warning users): the entries that core TensorFlow expects