code: def __init__(
      self,
      ch0,
      ch1,
      use_bn=True,
      sample="down",
      activation=F.relu,
      dropout=False,
  ):
      self.use_bn = use_bn
      self.activation = activation
      self.dropout = dropout
      w = chainer.initializers.Normal(0.02)
      super(ConvBNR, self).__init__()
      with self.init_scope():
          if sample == "down":
              self.c = L.Convolution2D(ch0, ch1, 4, 2, 1, initialW=w)
          else:
              self.c = L.Deconvolution2D(ch0, ch1, 4, 2, 1, initialW=w)
          if use_bn:
              self.bn = L.BatchNormalization(ch1)
  
imports: from __future__ import print_function
  import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new ConvBNR object from inherited base classes: chainer with example: obj = ConvBNR(ch0, ch1, use_bn, sample, activation, dropout)