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);
  }
}

# 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. *

    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