code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      dilation=1,
      downsample=None,
      bn_momentum=0.1,
  ):
      super(Bottleneck, self).__init__()
      self.padding = dilation
      self.stride = stride
      self.downsample = downsample
  
      self.conv1 = nn.Conv2D(
          inplanes,
          planes,
          kernel_size=1,
          stride=stride,
          bias_attr=False,
      )
      self.bn1 = nn.BatchNorm2D(planes)
  
      self.conv2 = nn.Conv2D(
          planes,
          planes,
          kernel_size=3,
          stride=1,
          padding=self.padding,
          bias_attr=False,
          dilation=dilation,
      )
      self.bn2 = nn.BatchNorm2D(planes)
  
      self.conv3 = nn.Conv2D(
          planes, planes * 4, bias_attr=False, kernel_size=1
      )
      self.bn3 = nn.BatchNorm2D(planes * 4)
  
      self.relu = nn.ReLU()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
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, dilation, downsample, bn_momentum)