code: def forward(self, x):
      inputs = self.backbone(x)
  
      inputs0 = self.conv0(inputs[0])
      inputs1 = F.interpolate(
          self.conv1(inputs[1]),
          size=inputs[0].shape[2:],
          mode="bilinear",
          align_corners=True,
      )
      inputs2 = F.interpolate(
          self.conv21(inputs[2]),
          scale_factor=2,
          mode="bilinear",
          align_corners=True,
      )
      inputs2 = F.interpolate(
          self.conv22(inputs2),
          size=inputs[0].shape[2:],
          mode="bilinear",
          align_corners=True,
      )
      inputs3 = F.interpolate(
          self.conv31(inputs[3]),
          scale_factor=2,
          mode="bilinear",
          align_corners=True,
      )
      inputs3 = F.interpolate(
          self.conv32(inputs3),
          scale_factor=2,
          mode="bilinear",
          align_corners=True,
      )
      inputs3 = F.interpolate(
          self.conv33(inputs3),
          size=inputs[0].shape[2:],
          mode="bilinear",
          align_corners=True,
      )
      inputs2 = inputs2 + inputs3
      inputs1 = inputs1 + inputs2
      inputs0 = inputs0 + inputs1
  
      feats = self.mlahead(inputs0, inputs1, inputs2, inputs3)
      logit = self.cls(feats)
      logit_list = [logit]
  
      if self.training:
          logit_list.append(self.aux_head(inputs[2]))
  
      logit_list = [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=True,
          )
          for logit in logit_list
      ]
      return logit_list
  
file_path: PaddleSeg/paddleseg/models/mla_transformer.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the MLATransformer obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list