class_docstr: Bottleneck for SENet154.
class_docstr_tok: ["Bottleneck","for","SENet154","."]
code: def __init__(
      self,
      inplanes,
      planes,
      groups,
      reduction,
      stride=1,
      downsample=None,
  ):
      super(SEBottleneck, self).__init__()
      self.conv1 = nn.Conv2d(
          inplanes, planes * 2, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(planes * 2)
      self.conv2 = nn.Conv2d(
          planes * 2,
          planes * 4,
          kernel_size=3,
          stride=stride,
          padding=1,
          groups=groups,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(planes * 4)
      self.conv3 = nn.Conv2d(
          planes * 4, planes * 4, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.se_module = SEModule(planes * 4, reduction=reduction)
      self.downsample = downsample
      self.stride = stride
  
imports: from collections import OrderedDict
  import torch.nn
  from torch.utils import model_zoo
label_desc: Use image node_id 1 to create a new SEBottleneck object from inherited base classes: Bottleneck with example: obj = SEBottleneck(inplanes, planes, groups, reduction, stride, downsample)