code: def fbeta(y_true, y_pred, beta=1):
      from tensorflow.keras import backend as K
  
      if beta < 0:
          raise ValueError(
              "The lowest choosable beta is zero (only precision)."
          )
  
      # If there are no true positives, fix the F score at 0 like sklearn.
      # if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
      #    return 0
  
      p = precision(y_true, y_pred)
      r = recall(y_true, y_pred)
      bb = beta**2
      fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
      return fbeta_score
  
file_path: talos/talos/metrics/keras_metrics.py
label_desc: Use image node_id 11 for calling a global function with example usage: fbeta(y_true, y_pred, beta) and returns: fbeta_score