def sg_aconv(tensor, opt):
    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`.
    """
    # 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