def METHOD_NAME(
    self,
    input_ids: Optional[Tensor] = None,
    token_type_ids: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
    attention_mask: Optional[Tensor] = None,
    inputs_embeds: Optional[Tensor] = None,
    labels: Optional[Tensor] = None,
    output_hidden_states: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
):
    outputs = self.ernie(
        input_ids,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        attention_mask=attention_mask,
        inputs_embeds=inputs_embeds,
        past_key_values=past_key_values,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=True,
    )
    pooled_output = outputs[1]
    pooled_output = self.dropout(pooled_output)
    logits = self.classifier(pooled_output)
    loss = None
    if labels is not None:
        if self.num_labels == 1:
            loss_fct = paddle.nn.MSELoss()
            loss = loss_fct(logits, labels)
        elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
            loss_fct = paddle.nn.CrossEntropyLoss()
            loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
        else:
            loss_fct = paddle.nn.BCEWithLogitsLoss()
            loss = loss_fct(logits, labels)
    return SequenceClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )