    def forward(
        self,
        src_tokens,
        features_only=False,
        return_all_hiddens=False,
        masked_tokens=None,
        lang_id=None,
        **unused,
    ):
        """
        Args:
            src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
            features_only (bool, optional): skip LM head and just return
                features. If True, the output will be of shape
                `(batch, src_len, embed_dim)`.
            return_all_hiddens (bool, optional): also return all of the
                intermediate hidden states (default: False).

        Returns:
            tuple:
                - the LM output of shape `(batch, src_len, vocab)`
                - a dictionary of additional data, where 'inner_states'
                  is a list of hidden states. Note that the hidden
                  states have shape `(src_len, batch, vocab)`.
        """
        x, extra = self.extract_features(
            src_tokens, return_all_hiddens=return_all_hiddens, lang_id=lang_id
        )
        if not features_only:
            x = self.output_layer(x, masked_tokens=masked_tokens)
        return x, extra
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
features_only (bool, optional): skip LM head and just return
features. If True, the output will be of shape
`(batch, src_len, embed_dim)`.
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).

Returns:
tuple:
- the LM output of shape `(batch, src_len, vocab)`
- a dictionary of additional data, where 'inner_states'
is a list of hidden states. Note that the hidden
states have shape `(src_len, batch, vocab)`.
fairseq/models/xmod/model.py