def infer_output_norm(module, output_norm=None):
    """
    Infer the output norm (string and module) needed on the module gvien desired
    output normalization.
    """
    if output_norm == module.output_norm():
        # output_norm already matches module.output_norm().
        return (None, NoOp())

    if output_norm is None and module.output_norm() is not None:
        logger = logging.getLogger("infer_output_norm()")
        logger.warning(
            "trying to set output_norm ({}) ".format(output_norm)
            + "but got module.output_norm() ({}), ".format(module.output_norm())
            + "the combined output_norm() will be ({})".format(module.output_norm())
        )
        return (None, NoOp())

    if output_norm == "log_softmax":
        if module.output_norm() is not None:
            raise ValueError(
                "incompatible output_norm ({}) ".format(output_norm)
                + "and module.output_norm() ({})".format(module.output_norm())
            )
        else:
            return ("log_softmax", torch.nn.LogSoftmax(dim=-1))

    if output_norm == "softmax":
        if module.output_norm() is not None:
            raise ValueError(
                "incompatible output_norm ({}) ".format(output_norm)
                + "and module.output_norm() ({})".format(module.output_norm())
            )
        else:
            return ("softmax", torch.nn.Softmax(dim=-1))

    raise ValueError(
        "output_norm ({}) not in ".format(output_norm)
        + "supported list = [None, softmax, log_softmax]"
    )
Infer the output norm (string and module) needed on the module gvien desired
output normalization.
fairseq/models/speech_to_text/utils.py