src/transformers/models/flaubert/modeling_tf_flaubert.py/TFFlaubertWithLMHeadModel/call
class TFFlaubertWithLMHeadModel:    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,
        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,
            training=training,
            kwargs_call=kwargs,
        )
        transformer_outputs = self.transformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            langs=inputs["langs"],
            token_type_ids=inputs["token_type_ids"],
            position_ids=inputs["position_ids"],
            lengths=inputs["lengths"],
            cache=inputs["cache"],
            head_mask=inputs["head_mask"],
            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"],
        )
        output = transformer_outputs[0]
        outputs = self.pred_layer(output)

        if not inputs["return_dict"]:
            return (outputs,) + transformer_outputs[1:]

        return TFFlaubertWithLMHeadModelOutput(
            logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions
        )