class_docstr: Base Model for Semantic Segmentation
  
  Parameters
  ----------
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
docstr: forwarding pre-trained network
docstr_tok: ["forwarding","pre-trained","network"]
code: def base_forward(self, x):
      """forwarding pre-trained network"""
      x = self.pretrained.conv1(x)
      x = self.pretrained.bn1(x)
      x = self.pretrained.relu(x)
      x = self.pretrained.maxpool(x)
      c1 = self.pretrained.layer1(x)
      c2 = self.pretrained.layer2(c1)
      c3 = self.pretrained.layer3(c2)
      c4 = self.pretrained.layer4(c3)
      return c1, c2, c3, c4
  
imports: import torch.nn
  from pywick.models.segmentation.da_basenets.resnetv1b import resnet50_v1s, resnet101_v1s, resnet152_v1s
label_desc: Use image node_id 2 for calling the SegBaseModel obj's underlying member method code with example usage: obj.base_forward(x) and returns: c1,  c2,  c3,  c4