src/transformers/models/funnel/modeling_funnel.py/FunnelModel/forward
class FunnelModel:    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # TODO: deal with head_mask
        if inputs_embeds is None:
            inputs_embeds = self.embeddings(input_ids)

        encoder_outputs = self.encoder(
            inputs_embeds,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            output_attentions=output_attentions,
            output_hidden_states=True,
            return_dict=return_dict,
        )

        decoder_outputs = self.decoder(
            final_hidden=encoder_outputs[0],
            first_block_hidden=encoder_outputs[1][self.config.block_sizes[0]],
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            idx = 0
            outputs = (decoder_outputs[0],)
            if output_hidden_states:
                idx += 1
                outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
            if output_attentions:
                idx += 1
                outputs = outputs + (encoder_outputs[2] + decoder_outputs[idx],)
            return outputs

        return BaseModelOutput(
            last_hidden_state=decoder_outputs[0],
            hidden_states=(encoder_outputs.hidden_states + decoder_outputs.hidden_states)
            if output_hidden_states
            else None,
            attentions=(encoder_outputs.attentions + decoder_outputs.attentions) if output_attentions else None,
        )