src/transformers/models/lxmert/modeling_tf_lxmert.py/TFLxmertModel/call
class TFLxmertModel:    def call(
        self,
        input_ids=None,
        visual_feats=None,
        visual_pos=None,
        attention_mask=None,
        visual_attention_mask=None,
        token_type_ids=None,
        inputs_embeds=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,
            visual_feats=visual_feats,
            visual_pos=visual_pos,
            attention_mask=attention_mask,
            visual_attention_mask=visual_attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.lxmert(
            input_ids=inputs["input_ids"],
            visual_feats=inputs["visual_feats"],
            visual_pos=inputs["visual_pos"],
            attention_mask=inputs["attention_mask"],
            visual_attention_mask=inputs["visual_attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )

        return outputs