code: def sg_aconv(tensor, opt):
      r
      # default options
      opt += tf.sg_opt(size=(3, 3), rate=2, pad='SAME')
      opt.size = opt.size if isinstance(opt.size, (tuple, list)) else [opt.size, opt.size]
  
      # parameter tf.sg_initializer
      w = tf.sg_initializer.he_uniform('W', (opt.size[0], opt.size[1], opt.in_dim, opt.dim),
                                       regularizer=opt.regularizer, summary=opt.summary)
      b = tf.sg_initializer.constant('b', opt.dim, summary=opt.summary) if opt.bias else 0
  
      # apply convolution
      out = tf.nn.atrous_conv2d(tensor, w, rate=opt.rate, padding=opt.pad) + b
  
      return out
docs: r"""Applies a 2-D atrous (or dilated) convolution.
      
      Args:
        tensor: A 4-D `Tensor` (automatically passed by decorator).
        opt:
          size: A tuple/list of positive integers of length 2 representing `[kernel height, kernel width]`.
            Can be an integer if both values are the same.
            If not specified, (3, 3) is set automatically.
          rate: A positive integer. The stride with which we sample input values across
            the `height` and `width` dimensions. Default is 2.
          in_dim: A positive `integer`. The size of input dimension.
          dim: A positive `integer`. The size of output dimension.
          pad: Either `SAME` (Default) or `VALID`.
          bias: Boolean. If True, biases are added.
          regularizer:  A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
            will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
          summary: If True, summaries are added. The default is True.
  
      Returns:
        A `Tensor` with the same type as `tensor`.