def METHOD_NAME(self, hidden_states: Tensor,
            references: Tensor) -> Tuple[Tensor, Tensor]:
    """"Forward function.
    Args:
        hidden_states (Tensor): Features from transformer decoder. If
            `return_intermediate_dec` is True output has shape
            (num_decoder_layers, bs, num_queries, dim), else has shape (1,
            bs, num_queries, dim) which only contains the last layer
            outputs.
        references (Tensor): References from transformer decoder, has
            shape (bs, num_queries, 2).
    Returns:
        tuple[Tensor]: results of head containing the following tensor.
        - layers_cls_scores (Tensor): Outputs from the classification head,
          shape (num_decoder_layers, bs, num_queries, cls_out_channels).
          Note cls_out_channels should include background.
        - layers_bbox_preds (Tensor): Sigmoid outputs from the regression
          head with normalized coordinate format (cx, cy, w, h), has shape
          (num_decoder_layers, bs, num_queries, 4).
    """
    references_unsigmoid = inverse_sigmoid(references)
    layers_bbox_preds = []
    for layer_id in range(hidden_states.shape[0]):
        tmp_reg_preds = self.fc_reg(
            self.activate(self.reg_ffn(hidden_states[layer_id])))
        tmp_reg_preds[..., :2] += references_unsigmoid
        outputs_coord = tmp_reg_preds.sigmoid()
        layers_bbox_preds.append(outputs_coord)
    layers_bbox_preds = torch.stack(layers_bbox_preds)
    layers_cls_scores = self.fc_cls(hidden_states)
    return layers_cls_scores, layers_bbox_preds