code: def support(y_target, y_predicted=None):
      """Support is the fraction of the true value
          in predictions and target values.
  
      Parameters
      -----------
      y_target : array-like, shape=[n_samples]
          True class labels.
      y_predicted : array-like, shape=[n_samples]
          Predicted class labels.
  
      Returns
      ----------
      score : float
          Support score in the range [0, 1]
  
      """
  
      if y_predicted is None:
          if y_target.ndim == 1:
              return (y_target == 1).sum() / float(y_target.shape[0])
          return (y_target == 1).all(axis=1).sum() / float(
              y_target.shape[0]
          )
      else:
          all_prod = np.column_stack([y_target, y_predicted])
          return (all_prod == 1).all(axis=1).sum() / float(
              all_prod.shape[0]
          )
  
label_desc: Use image node_id 2 for calling a global function with example usage: support(y_target, y_predicted) and returns: unknown, unknown, unknown