//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//
// GradientFunction* AddRegisterer(const ForwardOperation& op) {
//   // More complex gradient functions can use inputs/attrs etc. from the
//   // forward `op`.
//   return new AddGradientFunction;
// }
//
// Status RegisterGradients(GradientRegistry* registry) {

        case 128:
          *tf_dtype = TF_DataType::TF_COMPLEX128;
          return absl::OkStatus();
        default:
          return tensorflow::errors::InvalidArgument(
              "Unsupported Complex bits: ", dtype.bits);
      }
      break;
    default:
      return tensorflow::errors::InvalidArgument("Unsupported Type Codes: ",
                                                 dtype.code);
  }
}

    violates the use cases and security assumptions defined in the threat model.
    This will help us evaluate your report quickly, especially if the issue is
    complex.
  - Whether this vulnerability is public or known to third parties. If it is,
    please provide details.

We will try to fix the problems as soon as possible. Vulnerabilities will, in

# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is
# probably for the better. If a pipe is necessary for something, it is probably
# complex. Write a well-documented script under utilities/ to encapsulate the
# functionality instead.
tfrun() { "$@"; }

# Run all "tfrun" commands under Docker. See setup_docker.sh for details

  EXPECT_EQ(TF_STRING, static_cast<TF_DataType>(tensorflow::DT_STRING));
  EXPECT_EQ(TF_COMPLEX64, static_cast<TF_DataType>(tensorflow::DT_COMPLEX64));
  EXPECT_EQ(TF_COMPLEX, TF_COMPLEX64);
  EXPECT_EQ(TF_INT64, static_cast<TF_DataType>(tensorflow::DT_INT64));
  EXPECT_EQ(TF_BOOL, static_cast<TF_DataType>(tensorflow::DT_BOOL));
  EXPECT_EQ(TF_QINT8, static_cast<TF_DataType>(tensorflow::DT_QINT8));

little fidelity for improved speed. One can revert to the old behavior by
specifying the attribute `dct_method='INTEGER_ACCURATE'`. * `tf.complex_abs` has
been removed from the Python interface. `tf.abs` supports complex tensors and
should be used instead. * In the C++ API (in tensorflow/cc), Input, Output, etc.
have moved from the tensorflow::ops namespace to tensorflow. *

            string(TF_Message(s_)));

  TF_DeleteFunction(grad_func1);
  TF_DeleteFunction(grad_func2);
}

TEST_F(CApiFunctionTest, ImportFunctionDef) {
  /*
   * Using a fairly complex function with output names
   *
   *                  |  |  |
   *                  v  v  /
   *                  add  /
   *                   |  |
   *            +------+  |

    check_success: (Function) function that takes one argument and returns a
      boolean. Should return True if the value provided is considered valid. May
      contain a complex error message if error_msg does not provide enough
      information. In that case, set suppress_default_error to True.
    error_msg: (String) String with one and only one '%s'. Formatted with each