code: def __init__(self, block, layers, num_classes=1000, **kwargs):
      self.inplanes = 128
      super(ResNet, self).__init__()
      self.conv1 = conv3x3(3, 64, stride=2)
      self.bn1 = nn.BatchNorm2d(64)
      self.relu1 = nn.ReLU(inplace=True)
      self.conv2 = conv3x3(64, 64)
      self.bn2 = nn.BatchNorm2d(64)
      self.relu2 = nn.ReLU(inplace=True)
      self.conv3 = conv3x3(64, 128)
      self.bn3 = nn.BatchNorm2d(128)
      self.relu3 = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
      self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
      self.avgpool = nn.AvgPool2d(7, stride=1)
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import os
  import sys
  import torch
  import torch.nn
  import math
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(block, layers, num_classes)