code: def _compute(
      self,
      predictions,
      references,
      labels=None,
      pos_label=1,
      average="binary",
      sample_weight=None,
  ):
      score = f1_score(
          references,
          predictions,
          labels=labels,
          pos_label=pos_label,
          average=average,
          sample_weight=sample_weight,
      )
      return {"f1": float(score) if score.size == 1 else score}
  
imports: from sklearn.metrics import f1_score
  import datasets
label_desc: Use image node_id 2 for calling the F1 obj's underlying member method code with example usage: obj._compute(predictions, references, labels, pos_label, average, sample_weight) and returns: dict