src/transformers/models/xlnet/modeling_tf_xlnet.py/TFXLNetModel/call
class TFXLNetModel:    def call(
        self,
        input_ids=None,
        attention_mask=None,
        mems=None,
        perm_mask=None,
        target_mapping=None,
        token_type_ids=None,
        input_mask=None,
        head_mask=None,
        inputs_embeds=None,
        use_mems=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        training=False,
        **kwargs,
    ):
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            mems=mems,
            perm_mask=perm_mask,
            target_mapping=target_mapping,
            token_type_ids=token_type_ids,
            input_mask=input_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_mems=use_mems,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.transformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            mems=inputs["mems"],
            perm_mask=inputs["perm_mask"],
            target_mapping=inputs["target_mapping"],
            token_type_ids=inputs["token_type_ids"],
            input_mask=inputs["input_mask"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            use_mems=inputs["use_mems"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )

        return outputs