def call(
        self,
        input_ids: TFModelInputType | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        input_mask: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        mems: np.ndarray | tf.Tensor | None = None,
        perm_mask: np.ndarray | tf.Tensor | None = None,
        target_mapping: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        use_mems: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: bool = False,
    ) -> Union[TFXLNetForMultipleChoiceOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
        """

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

        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_input_mask = tf.reshape(input_mask, (-1, seq_length)) if input_mask is not None else None
        flat_inputs_embeds = (
            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
            if inputs_embeds is not None
            else None
        )
        transformer_outputs = self.transformer(
            flat_input_ids,
            flat_attention_mask,
            mems,
            perm_mask,
            target_mapping,
            flat_token_type_ids,
            flat_input_mask,
            head_mask,
            flat_inputs_embeds,
            use_mems,
            output_attentions,
            output_hidden_states,
            return_dict=return_dict,
            training=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 labels is None else self.hf_compute_loss(labels, reshaped_logits)

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

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

        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
        
dpkd/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py