src/transformers/models/gpt_neo/modeling_flax_gpt_neo.py/FlaxGPTNeoModule/__call__
class FlaxGPTNeoModule:    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        deterministic=True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        input_embeds = self.wte(input_ids.astype("i4"))
        position_embeds = self.wpe(position_ids.astype("i4"))

        hidden_states = input_embeds + position_embeds
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)

        outputs = self.h(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.ln_f(hidden_states)

        hidden_states = outputs[0]
        hidden_states = self.ln_f(hidden_states)

        if output_hidden_states:
            all_hidden_states = outputs[1] + (hidden_states,)
            outputs = (hidden_states, all_hidden_states) + outputs[2:]
        else:
            outputs = (hidden_states,) + outputs[1:]

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=outputs[1],
            attentions=outputs[-1],
        )