code: def __init__(
      self, inplanes, planes, stride=1, groups=1, downsample=None
  ):
      super(GroupBottleneck, self).__init__()
      self.conv1 = nn.Conv2d(
          inplanes, planes, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          groups=groups,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = nn.Conv2d(
          planes, planes * 2, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(planes * 2)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
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 GroupBottleneck object from inherited base classes: nn with example: obj = GroupBottleneck(inplanes, planes, stride, groups, downsample)