original_docstring: 
      3x3 depthwise separable version of the standard 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, 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      dw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the depthwise convolution block.
      pw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the pointwise convolution block.
      
docstring: 3x3 depthwise separable version of the standard 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, 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.
  bn_eps : float, default 1e-5
  Small float added to variance in Batch norm.
  dw_activation : function or str or None, default nn.ReLU(inplace=True)
  Activation function after the depthwise convolution block.
  pw_activation : function or str or None, default nn.ReLU(inplace=True)
  Activation function after the pointwise convolution block.
docstring_tokens: 3x3 depthwise separable version of the standard 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 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 . bn_eps : float default 1e - 5 Small float added to variance in Batch norm . dw_activation : function or str or None default nn . ReLU ( inplace = True ) Activation function after the depthwise convolution block . pw_activation : function or str or None default nn . ReLU ( inplace = True ) Activation function after the pointwise convolution block .
code: def dwsconv3x3_block(in_channels,
                       out_channels,
                       stride=1,
                       padding=1,
                       dilation=1,
                       bias=False,
                       bn_eps=1e-5,
                       dw_activation=(lambda: nn.ReLU(inplace=True)),
                       pw_activation=(lambda: nn.ReLU(inplace=True)),
                       **kwargs):
      """
      3x3 depthwise separable version of the standard 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, 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      dw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the depthwise convolution block.
      pw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the pointwise convolution block.
      """
      return DwsConvBlock(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          stride=stride,
          padding=padding,
          dilation=dilation,
          bias=bias,
          bn_eps=bn_eps,
          dw_activation=dw_activation,
          pw_activation=pw_activation,
          **kwargs)
code_tokens: def dwsconv3x3_block ( in_channels , out_channels , stride = 1 , padding = 1 , dilation = 1 , bias = False , bn_eps = 1e-5 , dw_activation = ( lambda : nn . ReLU ( inplace = True ) ) , pw_activation = ( lambda : nn . ReLU ( inplace = True ) ) , ** kwargs ) : return DwsConvBlock ( in_channels = in_channels , out_channels = out_channels , kernel_size = 3 , stride = stride , padding = padding , dilation = dilation , bias = bias , bn_eps = bn_eps , dw_activation = dw_activation , pw_activation = pw_activation , ** kwargs )
short_docstring: 3x3 depthwise separable version of the standard convolution block.
short_docstring_tokens: 3x3 depthwise separable version of the standard convolution block .
comment: """
      3x3 depthwise separable version of the standard 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, 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.
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      dw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the depthwise convolution block.
      pw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the pointwise convolution block.
      """