src/transformers/models/t5/modeling_t5.py/T5EncoderModel/forward
class T5EncoderModel:    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:

        Example::

            >>> from transformers import T5Tokenizer, T5EncoderModel
            >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
            >>> model = T5EncoderModel.from_pretrained('t5-small')
            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
            >>> outputs = model(input_ids=input_ids)
            >>> last_hidden_states = outputs.last_hidden_state
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        return encoder_outputs