code: def __init__(
      self, block, layers, groups=32, num_classes=1000, **kwargs
  ):
      self.inplanes = 128
      super(ResNeXt, 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, 128, layers[0], groups=groups
      )
      self.layer2 = self._make_layer(
          block, 256, layers[1], stride=2, groups=groups
      )
      self.layer3 = self._make_layer(
          block, 512, layers[2], stride=2, groups=groups
      )
      self.layer4 = self._make_layer(
          block, 1024, layers[3], stride=2, groups=groups
      )
      self.avgpool = nn.AvgPool2d(7, stride=1)
      self.fc = nn.Linear(1024 * 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.groups
              )
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
file_path: pywick/pywick/models/segmentation/testnets/lg_kernel_exfuse/seg_resnext.py
imports: import os
  import sys
  import torch
  import torch.nn
  import math
label_desc: Use image node_id 1 to create a new ResNeXt object from inherited base classes: nn with example: obj = ResNeXt(block, layers, groups, num_classes)