class_docstr: A convolution, batch norm, ReLU block.
  
  A block in a feedforward network that performs a
  convolution followed by batch normalization followed
  by a ReLU activation.
  
  For the convolution operation, a square filter size is used.
  
  Args:
      out_channels (int): The number of output channels.
      ksize (int): The size of the filter is ksize x ksize.
      pad (int): The padding to use for the convolution.
code: def __init__(self, out_channels, ksize, pad=1):
      super(Block, self).__init__()
      with self.init_scope():
          self.conv = L.Convolution2D(
              None, out_channels, ksize, pad=pad, nobias=True
          )
          self.bn = L.BatchNormalization(out_channels)
  
file_path: chainer/examples/static_graph_optimizations/cifar/models/VGG.py
imports: import chainer
  import chainer.functions
  import chainer.links
  from chainer import static_graph
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: chainer with example: obj = Block(out_channels, ksize, pad)