def sg_upconv(tensor, opt):
    r"""Applies a up convolution (or convolution transpose).
    
    Args:
      tensor: A 4-D `Tensor` (automatically passed by decorator).
      opt:
        size: A tuple/list of integers of length 2 representing `[kernel height, kernel width]`.
          Can be an integer if both values are the same.
          If not specified, (4, 4) is set implicitly.
        stride: A tuple/list of integers of length 2 or 4 representing stride dimensions.
          If the length is 2, i.e., (a, b), the stride is `[1, a, b, 1]`.
          If the length is 4, i.e., (a, b, c, d), the stride is `[a, b, c, d]`.
          Can be an integer. If the length is a, the stride is `[1, a, a, 1]`.
          Default value is [1, 2, 2, 1].
        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=(4, 4), stride=(1, 2, 2, 1), pad='SAME')
    opt.size = opt.size if isinstance(opt.size, (tuple, list)) else [opt.size, opt.size]
    opt.stride = opt.stride if isinstance(opt.stride, (tuple, list)) else [1, opt.stride, opt.stride, 1]
    opt.stride = [1, opt.stride[0], opt.stride[1], 1] if len(opt.stride) == 2 else opt.stride

    # parameter tf.sg_initializer
    w = tf.sg_initializer.he_uniform('W', (opt.size[0], opt.size[1], opt.dim, opt.in_dim),
                                     regularizer=opt.regularizer, summary=opt.summary)
    b = tf.sg_initializer.constant('b', opt.dim, summary=opt.summary) if opt.bias else 0

    # tedious shape handling for conv2d_transpose
    shape = tensor.get_shape().as_list()
    if opt.pad == "SAME":
        out_shape = [tf.shape(tensor)[0], shape[1] * opt.stride[1], shape[2] * opt.stride[2], opt.dim]
    else:
        out_shape = [tf.shape(tensor)[0], (shape[1] - 1) * opt.stride[1] + opt.size[0],
                     (shape[2] - 1) * opt.stride[2] + opt.size[1], opt.dim]

    # apply convolution
    out = tf.nn.conv2d_transpose(tensor, w, output_shape=tf.stack(out_shape),
                                 strides=opt.stride, padding=opt.pad) + b
    # reset shape is needed because conv2d_transpose() erase all shape information.
    # noinspection PyUnresolvedReferences
    out.set_shape([None, out_shape[1], out_shape[2], opt.dim])

    return out