src/transformers/models/mbart/modeling_tf_mbart.py/TFMBartModel/call
class TFMBartModel:    def call(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        use_cache=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,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
            kwargs_call=kwargs,
        )

        outputs = self.model(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            decoder_input_ids=inputs["decoder_input_ids"],
            decoder_attention_mask=inputs["decoder_attention_mask"],
            head_mask=inputs["head_mask"],
            decoder_head_mask=inputs["decoder_head_mask"],
            cross_attn_head_mask=inputs["cross_attn_head_mask"],
            encoder_outputs=inputs["encoder_outputs"],
            past_key_values=inputs["past_key_values"],
            inputs_embeds=inputs["inputs_embeds"],
            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
            use_cache=inputs["use_cache"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )

        return outputs