code: def normalize_to_unit_range(values):
      
      if not isinstance(values, np.ndarray) or values.ndim != 1:
          raise ValueError('`values` must be a 1D NumPy array')
  
      if len(values) < 2:
          raise ValueError('`values` must contain at least two values')
  
      min_ = np.min(values)
      max_ = np.max(values)
      range_ = max_ - min_
  
      if range_ == 0:
          raise ValueError('range of `values` is 0 -- cannot normalize')
  
      return (values - min_) / range_
docs: Bring a 1D NumPy array with at least two values in `values` to a linearly normalized range of [0, 1].