code: def __init__(self, inplanes, planes, stride=1, downsample=None):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Conv2d(
          inplanes, planes, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
      self.conv2 = nn.Conv2d(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
      self.conv3 = nn.Conv2d(
          planes, planes * self.expansion, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(
          planes * self.expansion, momentum=BN_MOMENTUM
      )
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
file_path: deep-learning-for-image-processing/pytorch_keypoint/HRNet/model/hrnet.py
imports: import torch.nn
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, stride, downsample)