code: def forward(self, x, mask_features):
      pos = self.pe_layer(x)
  
      mask = None
      hs, memory = self.transformer(
          self.input_proj(x), mask, self.query_embed.weight, pos
      )
  
      out = {}
      if self.mask_classification:
          outputs_class = self.class_embed(hs)
          out["pred_logits"] = outputs_class[-1]
  
      if self.aux_loss:
          mask_embed = self.mask_embed(hs)
  
          output_seg_masks = paddle.einsum(
              "lbqc,bchw->lbqhw", mask_embed, mask_features
          )
          out["pred_masks"] = output_seg_masks[-1]
          if self.mask_classification:
              out["aux_outputs"] = [
                  {"pred_logits": a, "pred_masks": b}
                  for a, b in zip(
                      outputs_class[:-1], output_seg_masks[:-1]
                  )
              ]
          else:
              out["aux_outputs"] = [
                  {"pred_masks": b} for b in output_seg_masks[:-1]
              ]
      else:
          mask_embed = self.mask_embed(hs[-1])
          output_seg_masks = paddle.einsum(
              "bqc,bchw->bqhw", mask_embed, mask_features
          )
          out["pred_masks"] = output_seg_masks
  
      return out
  
imports: import math
  import copy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
  from paddleseg.core.train import check_logits_losses
label_desc: Use image node_id 3 for calling the TransformerPredictor obj's underlying member method code with example usage: obj.forward(x, mask_features) and returns: out