src/transformers/models/xlm/modeling_tf_xlm.py/TFXLMForMultipleChoice/call
class TFXLMForMultipleChoice:    def call(
        self,
        input_ids=None,
        attention_mask=None,
        langs=None,
        token_type_ids=None,
        position_ids=None,
        lengths=None,
        cache=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
        **kwargs,
    ):
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )

        if inputs["input_ids"] is not None:
            num_choices = shape_list(inputs["input_ids"])[1]
            seq_length = shape_list(inputs["input_ids"])[2]
        else:
            num_choices = shape_list(inputs["inputs_embeds"])[1]
            seq_length = shape_list(inputs["inputs_embeds"])[2]

        flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
        flat_attention_mask = (
            tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
        )
        flat_token_type_ids = (
            tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
        )
        flat_position_ids = (
            tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
        )
        flat_langs = tf.reshape(inputs["langs"], (-1, seq_length)) if inputs["langs"] is not None else None
        flat_inputs_embeds = (
            tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
            if inputs["inputs_embeds"] is not None
            else None
        )

        if inputs["lengths"] is not None:
            logger.warning(
                "The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
                "attention mask instead.",
            )
            inputs["lengths"] = None

        transformer_outputs = self.transformer(
            flat_input_ids,
            flat_attention_mask,
            flat_langs,
            flat_token_type_ids,
            flat_position_ids,
            inputs["lengths"],
            inputs["cache"],
            inputs["head_mask"],
            flat_inputs_embeds,
            inputs["output_attentions"],
            inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        output = transformer_outputs[0]
        logits = self.sequence_summary(output)
        logits = self.logits_proj(logits)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))

        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)

        if not inputs["return_dict"]:
            output = (reshaped_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TFMultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )