original_docstring: 
      Depthwise convolution 3x3 layer.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      strides : 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.
      
docstring: Depthwise convolution 3x3 layer.
  Parameters.
  
  channels : int
  Number of input/output channels.
  strides : 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.
docstring_tokens: Depthwise convolution 3x3 layer . Parameters . channels : int Number of input / output channels . strides : 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 .
code: def depthwise_conv3x3(channels,
                        stride=1,
                        padding=1,
                        dilation=1,
                        bias=False):
      """
      Depthwise convolution 3x3 layer.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      strides : 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.
      """
      return nn.Conv2d(
          in_channels=channels,
          out_channels=channels,
          kernel_size=3,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=channels,
          bias=bias)
code_tokens: def depthwise_conv3x3 ( channels , stride = 1 , padding = 1 , dilation = 1 , bias = False ) : return nn . Conv2d ( in_channels = channels , out_channels = channels , kernel_size = 3 , stride = stride , padding = padding , dilation = dilation , groups = channels , bias = bias )
short_docstring: Depthwise convolution 3x3 layer.
short_docstring_tokens: Depthwise convolution 3x3 layer .
comment: """
      Depthwise convolution 3x3 layer.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      strides : 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.
      """