original_docstring: 
      Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
  
      This is used when we convert FC layers to equivalent Convolutional layers, BUT of a smaller size.
  
      :param tensor: tensor to be decimated
      :param m: list of decimation factors for each dimension of the tensor; None if not to be decimated along a dimension
      :return: decimated tensor
      
docstring: Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
  This is used when we convert FC layers to equivalent Convolutional layers, BUT of a smaller size.
docstring_tokens: Decimate a tensor by a factor ' m ' i . e . downsample by keeping every ' m ' th value . This is used when we convert FC layers to equivalent Convolutional layers BUT of a smaller size .
code: def decimate(tensor, m):
      """
      Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
  
      This is used when we convert FC layers to equivalent Convolutional layers, BUT of a smaller size.
  
      :param tensor: tensor to be decimated
      :param m: list of decimation factors for each dimension of the tensor; None if not to be decimated along a dimension
      :return: decimated tensor
      """
      assert tensor.dim() == len(m)
      for d in range(tensor.dim()):
          if m[d] is not None:
              tensor = tensor.index_select(dim=d,
                                           index=torch.arange(start=0, end=tensor.size(d), step=m[d]).long())
  
      return tensor
code_tokens: def decimate ( tensor , m ) : assert tensor . dim ( ) == len ( m ) for d in range ( tensor . dim ( ) ) : if m [ d ] is not None : tensor = tensor . index_select ( dim = d , index = torch . arange ( start = 0 , end = tensor . size ( d ) , step = m [ d ] ) . long ( ) ) return tensor
short_docstring: Decimate a tensor by a factor 'm', i.e.
short_docstring_tokens: Decimate a tensor by a factor ' m ' i . e .
comment: """
      Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
  
      This is used when we convert FC layers to equivalent Convolutional layers, BUT of a smaller size.
  
      :param tensor: tensor to be decimated
      :param m: list of decimation factors for each dimension of the tensor; None if not to be decimated along a dimension
      :return: decimated tensor
      """