src/transformers/models/electra/modeling_tf_electra.py/TFElectraMainLayer/call
class TFElectraMainLayer:    def call(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=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,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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,
        )

        if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif inputs["input_ids"] is not None:
            input_shape = shape_list(inputs["input_ids"])
        elif inputs["inputs_embeds"] is not None:
            input_shape = shape_list(inputs["inputs_embeds"])[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs["attention_mask"] is None:
            inputs["attention_mask"] = tf.fill(input_shape, 1)

        if inputs["token_type_ids"] is None:
            inputs["token_type_ids"] = tf.fill(input_shape, 0)

        hidden_states = self.embeddings(
            inputs["input_ids"],
            inputs["position_ids"],
            inputs["token_type_ids"],
            inputs["inputs_embeds"],
            training=inputs["training"],
        )
        extended_attention_mask = self.get_extended_attention_mask(
            inputs["attention_mask"], input_shape, hidden_states.dtype
        )
        inputs["head_mask"] = self.get_head_mask(inputs["head_mask"])

        if hasattr(self, "embeddings_project"):
            hidden_states = self.embeddings_project(hidden_states, training=inputs["training"])

        hidden_states = self.encoder(
            hidden_states,
            extended_attention_mask,
            inputs["head_mask"],
            inputs["output_attentions"],
            inputs["output_hidden_states"],
            inputs["return_dict"],
            training=inputs["training"],
        )

        return hidden_states