original_docstring:  A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution.
  
          Parameters
          ----------
          inputs : tf.Tensor
              input tensor
          strides : int
              strides in separable convolution
          double_filters : bool
              if True number of filters in 1x1 covolution will be doubled
          width_factor : float
              multiplier for the number of filters
          name : str
              scope name
  
          Returns
          -------
          tf.Tensor
          
docstring: A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution.
  Parameters
  
  
  
  Returns
  
  
docstring_tokens: A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution . Parameters Returns
code: def block(cls, inputs, strides=1, double_filters=False, width_factor=1, name=None, **kwargs):
          """ A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution.
  
          Parameters
          ----------
          inputs : tf.Tensor
              input tensor
          strides : int
              strides in separable convolution
          double_filters : bool
              if True number of filters in 1x1 covolution will be doubled
          width_factor : float
              multiplier for the number of filters
          name : str
              scope name
  
          Returns
          -------
          tf.Tensor
          """
          num_filters = int(cls.num_channels(inputs, kwargs.get('data_format')) * width_factor)
          filters = [num_filters, num_filters*2] if double_filters else num_filters
          return conv_block(inputs, layout='Cna cna', filters=filters, kernel_size=[3, 1], strides=[strides, 1],
                            name=name, **kwargs)
code_tokens: def block ( cls , inputs , strides = 1 , double_filters = False , width_factor = 1 , name = None , ** kwargs ) : num_filters = int ( cls . num_channels ( inputs , kwargs . get ( 'data_format' ) ) * width_factor ) filters = [ num_filters , num_filters * 2 ] if double_filters else num_filters return conv_block ( inputs , layout = 'Cna cna' , filters = filters , kernel_size = [ 3 , 1 ] , strides = [ strides , 1 ] , name = name , ** kwargs )
short_docstring: A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution.
short_docstring_tokens: A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution .
comment: """ A network building block consisting of a separable depthwise convolution and 1x1 pointwise covolution.
  
          Parameters
          ----------
          inputs : tf.Tensor
              input tensor
          strides : int
              strides in separable convolution
          double_filters : bool
              if True number of filters in 1x1 covolution will be doubled
          width_factor : float
              multiplier for the number of filters
          name : str
              scope name
  
          Returns
          -------
          tf.Tensor
          """