code: def recall_score(y_true, y_pred):
      """Recall metric.
  
      Only computes a batch-wise average of recall.
  
      Computes the recall, a metric for multi-label classification of
      how many relevant items are selected.
      """
      K = tf.keras.backend
      true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
      possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
      recall = true_positives / (possible_positives + K.epsilon())
      return recall
  
file_path: nlp-architect/nlp_architect/models/np_semantic_segmentation.py
imports: import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: recall_score(y_true, y_pred) and returns: recall