src/transformers/models/distilbert/modeling_flax_distilbert.py/FlaxDistilBertForMaskedLMModule/__call__
class FlaxDistilBertForMaskedLMModule:    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

        dlbrt_output = self.distilbert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            deterministic=deterministic,
            return_dict=return_dict,
        )
        hidden_states = dlbrt_output[0]
        prediction_logits = self.vocab_transform(hidden_states)
        prediction_logits = ACT2FN[self.config.activation](prediction_logits)
        prediction_logits = self.vocab_layer_norm(prediction_logits)

        if self.config.tie_word_embeddings:
            shared_embedding = self.distilbert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
            prediction_logits = self.vocab_projector(prediction_logits, shared_embedding.T)
        else:
            prediction_logits = self.vocab_projector(prediction_logits)

        if not return_dict:
            output = (prediction_logits,) + dlbrt_output[1:]
            return output

        return FlaxMaskedLMOutput(
            logits=prediction_logits,
            hidden_states=dlbrt_output.hidden_states,
            attentions=dlbrt_output.attentions,
        )