TransactionToken* token,
                                      uint64* file_size) {
  if (ops_->get_file_size == nullptr) {
    FileStatistics stat;
    Status status = Stat(fname, &stat);
    if (!status.ok()) return status;
    if (stat.is_directory)
      return errors::FailedPrecondition("Called GetFileSize on a directory");

    *file_size = stat.length;

    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {
    tsl::Set_TF_Status_from_Status(out_status, status);
  }
}

  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;
    }
    shape.push_back(dim);
  }
  return shape;
}

}  // namespace

    size_t chunk = size - offset;
    if (chunk > kPosixCopyFileBufferSize) chunk = kPosixCopyFileBufferSize;

    rc = read(src_fd, buffer.get(), chunk);
    if (rc < 0) return -1;

    int total_write = 0;
    int total_read = rc;
    while (total_write < total_read && rc > 0) {
      rc = write(dst_fd, buffer.get() + total_write, chunk - total_write);
      if (rc < 0) return -1;

      total_write += rc;

    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    string device_name(dev);
    if (device_name.find("GPU:0") != string::npos) {
      has_gpu0 = true;
    }
    if (device_name.find("GPU:1") != string::npos) {
      has_gpu1 = true;
    }
  }

  const char* kCPUDevice = "CPU:0";
  if (!has_gpu0 || !has_gpu1) {
    TF_DeleteDeviceList(devices);
    TF_DeleteTensor(t);

    EXPECT_EQ(n, block_size);
    EXPECT_EQ(offset % block_size, 0);
    size_t bytes_to_copy = 0;
    if (offset == 0) {
      // The first block (16 bytes) of the file.
      memset(buffer, 'x', n);
      bytes_to_copy = n;
      first_block = true;
    } else if (offset == block_size) {
      // The second block (8 bytes) of the file.
      bytes_to_copy = file_size - block_size;

  while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
    if (oper == feed) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (oper == add) {
      EXPECT_FALSE(found_add);
      found_add = true;
    } else if (oper == neg) {
      EXPECT_FALSE(found_neg);

  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {

      TFE_NewOp(context, "DestroyResourceOp", status), &TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return;
  TFE_OpAddInput(op.get(), handle_, status);
  if (TF_GetCode(status) != TF_OK) return;
  const char* device = TFE_TensorHandleDeviceName(handle_, status);
  if (TF_GetCode(status) != TF_OK) return;
  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return;
  int num_retvals = 0;

  // Run the model.
  TF_RETURN_IF_ERROR(
      RunModel(forward, ctx, inputs, model_outputs, use_function));
  AbstractTensorHandlePtr model_out(model_outputs[0]);

  TF_Tensor* model_out_tensor;
  TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor));
  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output