src/transformers/models/distilbert/modeling_flax_distilbert.py/FlaxDistilBertForSequenceClassificationModule/__call__
class FlaxDistilBertForSequenceClassificationModule:    def __call__(
        self,
        input_ids,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        # Model
        distilbert_output = self.distilbert(
            input_ids,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = ACT2FN["relu"](pooled_output)
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)  # (bs, dim)

        if not return_dict:
            return (logits,) + distilbert_output[1:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )