code: def handle_zeros_in_scale(scale, copy=True):
      ''' Makes sure that whenever scale is zero, we handle it correctly.
      This happens in most scalers when we have constant features.
      Adapted from sklearn.preprocessing.data'''
  
      # if we are fitting on 1D arrays, scale might be a scalar
      if np.isscalar(scale):
          if scale == .0:
              scale = 1.
          return scale
      elif isinstance(scale, np.ndarray):
          if copy:
              # New array to avoid side-effects
              scale = scale.copy()
          scale[scale == 0.0] = 1.0
      return scalf handle_zeros_in_scale(scale, copy=True):
      ''' Makes sure that whenever scale is zero, we handle it correctly.
      This happens in most scalers when we have constant features.
      Adapted from sklearn.preprocessing.data'''
  
      # if we are fitting on 1D arrays, scale might be a scalar
      if np.isscalar(scale):
          if scale == .0:
              scale = 1.
          return scale
      elif isinstance(scale, np.ndarray):
          if copy:
              # New array to avoid side-effects
              scale = scale.copy()
          scale[scale == 0.0] = 1.0
      return scale
docs: Makes sure that whenever scale is zero, we handle it correctly.
      This happens in most scalers when we have constant features.
      Adapted from sklearn.preprocessing.data