TFE_TensorHandleResolve(handle, status.get()), TF_DeleteTensor);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(TF_TensorType(actual_value.get()),
            static_cast<TF_DataType>(DataTypeToEnum<value_type>().value));
  EXPECT_EQ(expected_value,
            *static_cast<value_type*>(TF_TensorData(actual_value.get())));
}

  TF_Tensor* t = TFE_AllocateHostTensor(ctx, datatype, dims, num_dims, status);
  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

// Return a scalar tensor handle with given values.
template <class T, TF_DataType datatype>

bool GradientTape<Gradient, BackwardFunction, TapeTensor>::ShouldRecord(
    absl::Span<const int64_t> tensor_ids,
    absl::Span<const tensorflow::DataType> dtypes) const {
  CHECK_EQ(tensor_ids.size(), dtypes.size());
  for (int i = 0; i < tensor_ids.size(); ++i) {
    if (tensor_tape_.find(tensor_ids[i]) != tensor_tape_.end()) {
      if (IsDtypeTrainable(dtypes[i])) {
        return true;
      }