code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      dilation=1,
      downsample=None,
      previous_dilation=1,
      norm_layer=nn.BatchNorm2d,
  ):
      super(BottleneckV1b, self).__init__()
      self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
      self.bn1 = norm_layer(planes)
      self.conv2 = nn.Conv2d(
          planes, planes, 3, stride, dilation, dilation, bias=False
      )
      self.bn2 = norm_layer(planes)
      self.conv3 = nn.Conv2d(
          planes, planes * self.expansion, 1, bias=False
      )
      self.bn3 = norm_layer(planes * self.expansion)
      self.relu = nn.ReLU(True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new BottleneckV1b object from inherited base classes: nn with example: obj = BottleneckV1b(inplanes, planes, stride, dilation, downsample, previous_dilation, norm_layer)