if (TF_GetCode(status) != TF_OK) return;
  } else {
    TFE_OpReset(op_.get(), operation_name, device_.c_str(), status);
    if (TF_GetCode(status) != TF_OK) return;
  }
  TFE_OpAddAttrs(op_.get(), attributes);
  for (int input_index = 0; input_index < inputs.size(); ++input_index) {
    TFE_OpAddInput(op_.get(), inputs[input_index], status);
    if (TF_GetCode(status) != TF_OK) return;
  }

    // Special-cased operation for un-packing one parallel tensor into
    // per-device tensors.
    OpPtr op(TFE_NewOp(context, operation_name, status));
    TFE_OpAddAttrs(op.get(), attributes);
    int expected_outputs = TFE_OpGetOutputLength(op.get(), "outputs", status);
    if (TF_GetCode(status) != TF_OK) return result;
    if (expected_outputs != parallel_device.num_underlying_devices()) {

TF_CAPI_EXPORT extern const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op);
// Add attributes in `attrs` to `op`.
//
// Does not overwrite or update existing attributes, but adds new ones.
TF_CAPI_EXPORT extern void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs);

// Serialize `attrs` as a tensorflow::NameAttrList protocol buffer (into `buf`),
// containing the op name and a map of its attributes.

  tensorflow::unwrap(ctx)->EndStep();
}

const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op) {
  return tensorflow::wrap(tensorflow::unwrap(op)->GetOpAttrs());
}

void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs) {
  tensorflow::unwrap(op)->AddAttrs(tensorflow::unwrap(attrs));
}

void TFE_OpAttrsSerialize(const TFE_OpAttrs* attrs, TF_Buffer* buf,