original_docstring: 
      3x3 asymmetric separable convolution block.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      padding : int, default 1
          Padding value for convolution layer.
      dilation : int, default 1
          Dilation value for convolution layer.
      groups : int, default 1
          Number of groups.
      bias : bool, default False
          Whether the layer uses a bias vector.
      lw_use_bn : bool, default True
          Whether to use BatchNorm layer (leftwise convolution block).
      rw_use_bn : bool, default True
          Whether to use BatchNorm layer (rightwise convolution block).
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      lw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the leftwise convolution block.
      rw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the rightwise convolution block.
      
docstring: 3x3 asymmetric separable convolution block.
  Parameters.
  
  channels : int
  Number of input/output channels.
  padding : int, default 1
  Padding value for convolution layer.
  dilation : int, default 1
  Dilation value for convolution layer.
  groups : int, default 1
  Number of groups.
  bias : bool, default False
  Whether the layer uses a bias vector.
  lw_use_bn : bool, default True
  Whether to use BatchNorm layer (leftwise convolution block).
  rw_use_bn : bool, default True
  Whether to use BatchNorm layer (rightwise convolution block).
  bn_eps : float, default 1e-5
  Small float added to variance in Batch norm.
  lw_activation : function or str or None, default nn.ReLU(inplace=True)
  Activation function after the leftwise convolution block.
  rw_activation : function or str or None, default nn.ReLU(inplace=True)
  Activation function after the rightwise convolution block.
docstring_tokens: 3x3 asymmetric separable convolution block . Parameters . channels : int Number of input / output channels . padding : int default 1 Padding value for convolution layer . dilation : int default 1 Dilation value for convolution layer . groups : int default 1 Number of groups . bias : bool default False Whether the layer uses a bias vector . lw_use_bn : bool default True Whether to use BatchNorm layer ( leftwise convolution block ) . rw_use_bn : bool default True Whether to use BatchNorm layer ( rightwise convolution block ) . bn_eps : float default 1e - 5 Small float added to variance in Batch norm . lw_activation : function or str or None default nn . ReLU ( inplace = True ) Activation function after the leftwise convolution block . rw_activation : function or str or None default nn . ReLU ( inplace = True ) Activation function after the rightwise convolution block .
code: def asym_conv3x3_block(padding=1,
                         **kwargs):
      """
      3x3 asymmetric separable convolution block.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      padding : int, default 1
          Padding value for convolution layer.
      dilation : int, default 1
          Dilation value for convolution layer.
      groups : int, default 1
          Number of groups.
      bias : bool, default False
          Whether the layer uses a bias vector.
      lw_use_bn : bool, default True
          Whether to use BatchNorm layer (leftwise convolution block).
      rw_use_bn : bool, default True
          Whether to use BatchNorm layer (rightwise convolution block).
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      lw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the leftwise convolution block.
      rw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the rightwise convolution block.
      """
      return AsymConvBlock(
          kernel_size=3,
          padding=padding,
          **kwargs)
code_tokens: def asym_conv3x3_block ( padding = 1 , ** kwargs ) : return AsymConvBlock ( kernel_size = 3 , padding = padding , ** kwargs )
short_docstring: 3x3 asymmetric separable convolution block.
short_docstring_tokens: 3x3 asymmetric separable convolution block .
comment: """
      3x3 asymmetric separable convolution block.
  
      Parameters:
      ----------
      channels : int
          Number of input/output channels.
      padding : int, default 1
          Padding value for convolution layer.
      dilation : int, default 1
          Dilation value for convolution layer.
      groups : int, default 1
          Number of groups.
      bias : bool, default False
          Whether the layer uses a bias vector.
      lw_use_bn : bool, default True
          Whether to use BatchNorm layer (leftwise convolution block).
      rw_use_bn : bool, default True
          Whether to use BatchNorm layer (rightwise convolution block).
      bn_eps : float, default 1e-5
          Small float added to variance in Batch norm.
      lw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the leftwise convolution block.
      rw_activation : function or str or None, default nn.ReLU(inplace=True)
          Activation function after the rightwise convolution block.
      """