class_docstr: ResNet model.
class_docstr_tok: ["ResNet","model","."]
code: def _construct_imagenet(self):
      g, gw = cfg.RESNET.NUM_GROUPS, cfg.RESNET.WIDTH_PER_GROUP
      (d1, d2, d3, d4) = _IN_STAGE_DS[cfg.MODEL.DEPTH]
      w_b = gw * g
      self.stem = ResStemIN(3, 64)
      self.s1 = ResStage(64, 256, stride=1, d=d1, w_b=w_b, groups=g)
      self.s2 = ResStage(
          256, 512, stride=2, d=d2, w_b=w_b * 2, groups=g
      )
      self.s3 = ResStage(
          512, 1024, stride=2, d=d3, w_b=w_b * 4, groups=g
      )
      self.s4 = ResStage(
          1024, 2048, stride=2, d=d4, w_b=w_b * 8, groups=g
      )
      self.head = ResHead(2048, cfg.MODEL.NUM_CLASSES)
  
imports: from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, gap2d, gap2d_cx, init_weights, linear, linear_cx, norm2d, norm2d_cx, pool2d, pool2d_cx
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the ResNet obj's underlying member method code with example usage: obj._construct_imagenet() without return types