def METHOD_NAME(val, num_classes=81):
    # bias
    if val.dim() == 1:
        if val.size(0) % num_classes == 0:
            new_val = val[:num_classes - 1]
        else:
            new_val = val
    # weight
    else:
        out_channels, in_channels = val.shape[:2]
        # conv_logits
        if out_channels % num_classes == 0:
            new_val = val.reshape(num_classes, in_channels, *val.shape[2:])[1:]
            new_val = new_val.reshape(-1, *val.shape[1:])
        # agnostic
        else:
            new_val = val
    return new_val