class_docstr: ISONet, a modified ResNet model.
class_docstr_tok: ["ISONet",",","a","modified","ResNet","model","."]
code: def _construct(self, net_params):
      # Setting for ImageNet image size. To override if different.
      # Retrieve the number of blocks per stage
      (d1, d2, d3, d4) = _IN_STAGE_DS[net_params["depths"]]  # _depths
      # Compute the initial bottleneck width
      # Stem: (N, 3, 224, 224) -> (N, 64, 56, 56)
      self.stem = ResStem(w_in=3, w_out=64, net_params=net_params)
      # Stage 1: (N, 64, 56, 56) -> (N, 256, 56, 56)
      self.s1 = ResStage(
          w_in=64, w_out=64, stride=1, net_params=net_params, d=d1
      )
      # Stage 2: (N, 256, 56, 56) -> (N, 512, 28, 28)
      self.s2 = ResStage(
          w_in=64, w_out=128, stride=2, net_params=net_params, d=d2
      )
      # Stage 3: (N, 512, 56, 56) -> (N, 1024, 14, 14)
      self.s3 = ResStage(
          w_in=128, w_out=256, stride=2, net_params=net_params, d=d3
      )
      # Stage 4: (N, 1024, 14, 14) -> (N, 2048, 7, 7)
      self.s4 = ResStage(
          w_in=256, w_out=512, stride=2, net_params=net_params, d=d4
      )
      # Head: (N, 2048, 7, 7) -> (N, num_classes)
      self.head = ResHead(w_in=512, net_params=net_params)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ISONet obj's underlying member method code with example usage: obj._construct(net_params) without return types