code: def __init__(
      self, inplanes, midplanes, outplanes, stride, dilation, downsample
  ):
      super(_Bottleneck, self).__init__()
      self.reduce = _ConvBatchNormReluBlock(
          inplanes, midplanes, 1, stride, 0, 1
      )
      self.conv3x3 = _ConvBatchNormReluBlock(
          midplanes, midplanes, 3, 1, dilation, dilation
      )
      self.increase = _ConvBatchNormReluBlock(
          midplanes, outplanes, 1, 1, 0, 1, relu=False
      )
      self.downsample = downsample
      if self.downsample:
          self.proj = _ConvBatchNormReluBlock(
              inplanes, outplanes, 1, stride, 0, 1, relu=False
          )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .None import seg_resnet
  from .None import seg_resnext
label_desc: Use image node_id 1 to create a new _Bottleneck object from inherited base classes: nn with example: obj = _Bottleneck(inplanes, midplanes, outplanes, stride, dilation, downsample)