def METHOD_NAME(
    self, name, num_classes=None, inner_dim=None, **kwargs
):
    """Register a classification head."""
    if name in self.classification_heads:
        prev_num_classes = self.classification_heads[name].out_proj.out_features
        prev_inner_dim = self.classification_heads[name].dense.out_features
        if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
            logger.warning(
                're-registering head "{}" with num_classes {} (prev: {}) '
                "and inner_dim {} (prev: {})".format(
                    name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
                )
            )
    embed_dim = self.cfg.pretrained_model_args.model.embed_dim
    self.classification_heads[name] = RobertaClassificationHead(
        input_dim=embed_dim,
        inner_dim=inner_dim or embed_dim,
        num_classes=num_classes,
        activation_fn=self.cfg.pooler_activation_fn,
        pooler_dropout=self.cfg.pooler_dropout,
        q_noise=self.cfg.quant_noise_pq,
        qn_block_size=self.cfg.quant_noise_pq_block_size,
        do_spectral_norm=self.cfg.spectral_norm_classification_head,
    )