code: def forward(self, x):
      input_shape = x.shape[-2:]
      # contract: features is a dict of tensors
      features = self.backbone(x)
  
      result = OrderedDict()
      x = features["out"]
      x = self.classifier(x)
      x = F.interpolate(
          x, size=input_shape, mode="bilinear", align_corners=False
      )
      result["out"] = x
  
      if self.aux_classifier is not None:
          x = features["aux"]
          x = self.aux_classifier(x)
          x = F.interpolate(
              x, size=input_shape, mode="bilinear", align_corners=False
          )
          result["aux"] = x
  
      return result
  
imports: from collections import OrderedDict
  import torch
  from torch import nn
  from torch.jit.annotations import Dict
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the _SimpleSegmentationModel obj's underlying member method code with example usage: obj.forward(x) and returns: result