original_docstring: 
      Depthwise version of the standard convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      kernel_size : int or tuple/list of 2 int
          Convolution window size.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int, or tuple/list of 2 int, or tuple/list of 4 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function or name of activation function.
      
docstring: Depthwise version of the standard convolution block.
  Parameters.
  
  in_channels : int
  Number of input channels.
  out_channels : int
  Number of output channels.
  kernel_size : int or tuple/list of 2 int
  Convolution window size.
  stride : int or tuple/list of 2 int, default 1
  Strides of the convolution.
  padding : int, or tuple/list of 2 int, or tuple/list of 4 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.
  bn_eps : float, default 1e-5
  Small float added to variance in Batch norm.
  activation : function or str or None, default nn.ReLU(inplace=True)
  Activation function or name of activation function.
docstring_tokens: Depthwise version of the standard convolution block . Parameters . in_channels : int Number of input channels . out_channels : int Number of output channels . kernel_size : int or tuple / list of 2 int Convolution window size . stride : int or tuple / list of 2 int default 1 Strides of the convolution . padding : int or tuple / list of 2 int or tuple / list of 4 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 . bn_eps : float default 1e - 5 Small float added to variance in Batch norm . activation : function or str or None default nn . ReLU ( inplace = True ) Activation function or name of activation function .
code: def dwconv_block(in_channels,
                   out_channels,
                   kernel_size,
                   stride=1,
                   padding=1,
                   dilation=1,
                   bias=False,
                   use_bn=True,
                   bn_eps=1e-5,
                   activation=(lambda: nn.ReLU(inplace=True))):
      """
      Depthwise version of the standard convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      kernel_size : int or tuple/list of 2 int
          Convolution window size.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int, or tuple/list of 2 int, or tuple/list of 4 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function or name of activation function.
      """
      return ConvBlock(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=out_channels,
          bias=bias,
          use_bn=use_bn,
          bn_eps=bn_eps,
          activation=activation)
code_tokens: def dwconv_block ( in_channels , out_channels , kernel_size , stride = 1 , padding = 1 , dilation = 1 , bias = False , use_bn = True , bn_eps = 1e-5 , activation = ( lambda : nn . ReLU ( inplace = True ) ) ) : return ConvBlock ( in_channels = in_channels , out_channels = out_channels , kernel_size = kernel_size , stride = stride , padding = padding , dilation = dilation , groups = out_channels , bias = bias , use_bn = use_bn , bn_eps = bn_eps , activation = activation )
short_docstring: Depthwise version of the standard convolution block.
short_docstring_tokens: Depthwise version of the standard convolution block .
comment: """
      Depthwise version of the standard convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      kernel_size : int or tuple/list of 2 int
          Convolution window size.
      stride : int or tuple/list of 2 int, default 1
          Strides of the convolution.
      padding : int, or tuple/list of 2 int, or tuple/list of 4 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function or name of activation function.
      """