original_docstring: 
      3x3 version of the pre-activated convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int or tuple/list of 2 int, default 1
          Padding value for convolution layer.
      dilation : int or tuple/list of 2 int, default 1
          Dilation value for convolution layer.
      bias : bool, default False
          Whether the layer uses a bias vector.
      use_bn : bool, default True
          Whether to use BatchNorm layer.
      return_preact : bool, default False
          Whether return pre-activation.
      activate : bool, default True
          Whether activate the convolution block.
      
docstring: 3x3 version of the pre-activated convolution block.
  Parameters.
  
  in_channels : int
  Number of input channels.
  out_channels : int
  Number of output channels.
  stride : int or tuple/list of 2 int, default 1
  Strides of the convolution.
  padding : int or tuple/list of 2 int, default 1
  Padding value for convolution layer.
  dilation : int or tuple/list of 2 int, default 1
  Dilation value for convolution layer.
  bias : bool, default False
  Whether the layer uses a bias vector.
  use_bn : bool, default True
  Whether to use BatchNorm layer.
  return_preact : bool, default False
  Whether return pre-activation.
  activate : bool, default True
  Whether activate the convolution block.
docstring_tokens: 3x3 version of the pre - activated convolution block . Parameters . in_channels : int Number of input channels . out_channels : int Number of output channels . stride : int or tuple / list of 2 int default 1 Strides of the convolution . padding : int or tuple / list of 2 int default 1 Padding value for convolution layer . dilation : int or tuple / list of 2 int default 1 Dilation value for convolution layer . bias : bool default False Whether the layer uses a bias vector . use_bn : bool default True Whether to use BatchNorm layer . return_preact : bool default False Whether return pre - activation . activate : bool default True Whether activate the convolution block .
code: def pre_conv3x3_block(in_channels,
                        out_channels,
                        stride=1,
                        padding=1,
                        dilation=1,
                        bias=False,
                        use_bn=True,
                        return_preact=False,
                        activate=True):
      """
      3x3 version of the pre-activated convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int or tuple/list of 2 int, default 1
          Padding value for convolution layer.
      dilation : int or tuple/list of 2 int, default 1
          Dilation value for convolution layer.
      bias : bool, default False
          Whether the layer uses a bias vector.
      use_bn : bool, default True
          Whether to use BatchNorm layer.
      return_preact : bool, default False
          Whether return pre-activation.
      activate : bool, default True
          Whether activate the convolution block.
      """
      return PreConvBlock(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          stride=stride,
          padding=padding,
          dilation=dilation,
          bias=bias,
          use_bn=use_bn,
          return_preact=return_preact,
          activate=activate)
code_tokens: def pre_conv3x3_block ( in_channels , out_channels , stride = 1 , padding = 1 , dilation = 1 , bias = False , use_bn = True , return_preact = False , activate = True ) : return PreConvBlock ( in_channels = in_channels , out_channels = out_channels , kernel_size = 3 , stride = stride , padding = padding , dilation = dilation , bias = bias , use_bn = use_bn , return_preact = return_preact , activate = activate )
short_docstring: 3x3 version of the pre-activated convolution block.
short_docstring_tokens: 3x3 version of the pre - activated convolution block .
comment: """
      3x3 version of the pre-activated convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int or tuple/list of 2 int, default 1
          Padding value for convolution layer.
      dilation : int or tuple/list of 2 int, default 1
          Dilation value for convolution layer.
      bias : bool, default False
          Whether the layer uses a bias vector.
      use_bn : bool, default True
          Whether to use BatchNorm layer.
      return_preact : bool, default False
          Whether return pre-activation.
      activate : bool, default True
          Whether activate the convolution block.
      """