code: def target_color_type(y):
      
      ttype = type_of_target(y)
  
      if ttype.startswith(CONTINUOUS):
          return CONTINUOUS
  
      if ttype.startswith("binary"):
          return DISCRETE
  
      if ttype.startswith("multiclass"):
          if len(np.unique(y)) > MAX_DISCRETE_CLASSES:
              return CONTINUOUS
          return DISCRETE
  
      return UNKNOWN
docs: Determines the type of color space that will best represent the target
      variable y, e.g. either a discrete (categorical) color space or a
      continuous color space that requires a colormap. This function can handle
      both 1D or column vectors as well as multi-output targets.
  
      Parameters
      ----------
      y : array-like
          Must be a valid array-like data structure that can be passed to a
          scikit-learn supervised estimator.
  
      Returns
      -------
      color_type : string
          One of:
  
          * 'discrete': `y` is either a binary target or a multiclass target
            with <= 12 discrete classes.
          * 'continuous': `y` is an array-like of floats that are not all
            integers or a multiclass target with > 12 discrete classes.
          * 'unknown': `y` is array-like but none of the above. For example
            a multilabel-indicator or a 3D array. No exception is raised.