code: def __init__(self, in_size, ch, out_size, stride=2, groups=1):
      super(BottleNeckA, self).__init__()
      initialW = initializers.HeNormal()
  
      with self.init_scope():
          self.conv1 = L.Convolution2D(
              in_size, ch, 1, stride, 0, initialW=initialW, nobias=True
          )
          self.bn1 = L.BatchNormalization(ch)
          self.conv2 = L.Convolution2D(
              ch,
              ch,
              3,
              1,
              1,
              initialW=initialW,
              nobias=True,
              groups=groups,
          )
          self.bn2 = L.BatchNormalization(ch)
          self.conv3 = L.Convolution2D(
              ch, out_size, 1, 1, 0, initialW=initialW, nobias=True
          )
          self.bn3 = L.BatchNormalization(out_size)
  
          self.conv4 = L.Convolution2D(
              in_size,
              out_size,
              1,
              stride,
              0,
              initialW=initialW,
              nobias=True,
          )
          self.bn4 = L.BatchNormalization(out_size)
  
imports: import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
label_desc: Use image node_id 1 to create a new BottleNeckA object from inherited base classes: chainer with example: obj = BottleNeckA(in_size, ch, out_size, stride, groups)