}
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));
      if (!status->status.ok()) {
        // Contract is we always delete input_values[i].
        return false;
      }
      // Note: session->session is not modified if Extend() fails, so
      // we only set last_num_graph_nodes if it succeeds.

                      ", 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();
}

// For TFE_CustomDevice::copy_tensor_to_device in the parallel device
// registration.
//
// Since this function is used to satisfy the TFE_CustomDevice C API,
// device_info is passed in using a C-style generic. It must always be a
// ParallelDevice.
TFE_TensorHandle* CopyToParallelDevice(TFE_Context* context,
                                       TFE_TensorHandle* tensor,

//
// DeviceThread itself is thread-safe, in that StartExecute will block if there
// is a pending execution. Since StartExecute is equivalent to grabbing a lock,
// multiple DeviceThreads should always be accessed in the same order to avoid
// deadlocks.
class DeviceThread {
 public:
  // Starts a background thread waiting for `StartExecute`.
  explicit DeviceThread(const std::string& device, const bool is_async,

  EXPECT_TRUE(cache2.Lookup("d", &value));
  EXPECT_EQ(value, 4);
  EXPECT_TRUE(cache2.Lookup("e", &value));
  EXPECT_EQ(value, 5);
}

TEST(ExpiringLRUCacheTest, LookupOrCompute) {
  // max_age of 0 means we should always compute.
  uint64 num_compute_calls = 0;
  tf_gcs_filesystem::ExpiringLRUCache<int>::ComputeFunc compute_func =
      [&num_compute_calls](const string& key, int* value, TF_Status* status) {
        *value = num_compute_calls;

                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;

            tensorflow::DataTypeSize(tensorflow::DT_DOUBLE));
  EXPECT_EQ(TF_DataTypeSize(TF_STRING),
            tensorflow::DataTypeSize(tensorflow::DT_STRING));
  // Test with invalid type; should always return 0 as documented
  EXPECT_EQ(TF_DataTypeSize(static_cast<TF_DataType>(0)), 0);
}

TEST(CAPI, StatusEnum) {
  EXPECT_EQ(TF_OK, static_cast<TF_Code>(tensorflow::error::OK));

  }
  TFE_DeleteOp(add_op);

  TF_Tensor* t = TFE_TensorHandleResolve(retval, status);
  if (async) {
    if (forward_input) {
      // Since the input was forwarded, we released the input handle right away
      // and hence expect the input to be forwarded to the return tensor.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {
      // In async mode we expect forwarding to work without releasing the input

}

// Validates the read only memory region operations given by the plugin.
static Status ValidateHelper(const TF_ReadOnlyMemoryRegionOps* ops) {
  if (ops == nullptr) {
    // read only memory region support is always optional
    return OkStatus();
  }

  if (ops->cleanup == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `cleanup` operation on read "

  const std::string object;
  gcs::Client* gcs_client;  // not owned
  TempFile outfile;
  bool sync_need;
  // `offset` tells us how many bytes of this file are already uploaded to
  // server. If `offset == -1`, we always upload the entire temporary file.
  int64_t offset;
} GCSFile;

static void SyncImpl(const std::string& bucket, const std::string& object,
                     int64_t* offset, TempFile* outfile,