class_docstr: Bottleneck Unit
class_docstr_tok: ["Bottleneck","Unit"]
code: def __init__(
      self,
      in_channels,
      mid_channels,
      out_channels,
      stride,
      dilation,
      downsample,
  ):
      super(_Bottleneck, self).__init__()
      self.reduce = _ConvBatchNormReLU(
          in_channels, mid_channels, 1, stride, 0, 1
      )
      self.conv3x3 = _ConvBatchNormReLU(
          mid_channels, mid_channels, 3, 1, dilation, dilation
      )
      self.increase = _ConvBatchNormReLU(
          mid_channels, out_channels, 1, 1, 0, 1, relu=False
      )
      self.downsample = downsample
      if self.downsample:
          self.proj = _ConvBatchNormReLU(
              in_channels, out_channels, 1, stride, 0, 1, relu=False
          )
  
file_path: pywick/pywick/models/segmentation/testnets/resnet.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new _Bottleneck object from inherited base classes: nn with example: obj = _Bottleneck(in_channels, mid_channels, out_channels, stride, dilation, downsample)