forward_op_.attrs.Set(attr_name,
                        gtl::ArraySlice<const float>(values, num_values));
  return parent_op_->SetAttrFloatList(attr_name, values, num_values);
}
Status TapeOperation::SetAttrIntList(const char* attr_name,
                                     const int64_t* values, int num_values) {
  forward_op_.attrs.Set(
      attr_name, gtl::ArraySlice<const int64_t>(

    op_->node_builder.Attr(attr_name,
                           ArraySlice<const float>(values, num_values));
    return absl::OkStatus();
  }
  Status SetAttrIntList(const char* attr_name, const int64_t* values,
                        int num_values) override {
    op_->node_builder.Attr(
        attr_name, ArraySlice<const int64_t>(
                       reinterpret_cast<const int64_t*>(values), num_values));

  AbstractTensorHandle* AggregateGradients(
      gtl::ArraySlice<AbstractTensorHandle*> gradient_tensors) const override;

  // Calls the passed-in backward function.
  // op_type is the op's name provided in RecordOperation.
  Status CallBackwardFunction(
      const string& op_type, GradientFunction* gradient_function,
      const std::vector<int64_t>& unneeded_gradients,
      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,

  virtual int64_t NumElements(Gradient* tensor) const = 0;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  virtual Gradient* AggregateGradients(
      gtl::ArraySlice<Gradient*> gradient_tensors) const = 0;

  // Calls the passed-in backward function.
  //
  // `unneeded_gradients` contains sorted list of input indices for which a
  // gradient is not required.

using tensorflow::TensorShapeProto;
using tensorflow::VersionDef;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::OutOfRange;
using tensorflow::gtl::ArraySlice;
using tensorflow::strings::StrCat;

extern "C" {

// --------------------------------------------------------------------------
const char* TF_Version() { return TF_VERSION_STRING; }

                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(
                          reinterpret_cast<const tensorflow::DataType*>(values),
                          num_values));
}

void TF_AttrBuilderCheckCanRunOnDevice(TF_AttrBuilder* builder,