code: def __init__(
      self,
      name,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      output_padding=0,
      dilation=1,
      groups=1,
      use_bias=False,
      norm_layer=None,
      act=None,
      act_attr=None,
  ):
      super(SNConvTranspose, self).__init__()
      if use_bias:
          bias_attr = paddle.ParamAttr(name=name + "_bias")
      else:
          bias_attr = None
      self._sn_conv_transpose = spectral_norm(
          paddle.nn.Conv2DTranspose(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
              output_padding=output_padding,
              dilation=dilation,
              groups=groups,
              weight_attr=paddle.ParamAttr(name=name + "_weights"),
              bias_attr=bias_attr,
          )
      )
      if norm_layer:
          self._norm_layer = getattr(paddle.nn, norm_layer)(
              num_features=out_channels, name=name + "_bn"
          )
      else:
          self._norm_layer = None
      if act:
          if act_attr:
              self._act = getattr(paddle.nn, act)(
                  **act_attr, name=name + "_" + act
              )
          else:
              self._act = getattr(paddle.nn, act)(name=name + "_" + act)
      else:
          self._act = None
  
imports: import paddle
  import paddle.nn
  from arch.spectral_norm import spectral_norm
label_desc: Use image node_id 1 to create a new SNConvTranspose object from inherited base classes: nn with example: obj = SNConvTranspose(name, in_channels, out_channels, kernel_size, stride, padding, output_padding, dilation, groups, use_bias, norm_layer, act, act_attr)