code: def f1_score() -> EvaluationMetric:
      """
      This function will create a metric for evaluating `f1_score`_ for binary classification.
  
      This metric computes an aggregate score between 0 and 1 for the F1 score (F-measure) of a
      classification task. F1 score is defined as 2 * (precision * recall) / (precision + recall).
  
      .. _f1_score: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
      """
      return make_metric(
          eval_fn=_f1_score_eval_fn,
          greater_is_better=True,
          name="f1_score",
      )
  
imports: from mlflow.metrics.base import MetricValue
  from mlflow.metrics.metric_definitions import _accuracy_eval_fn, _ari_eval_fn, _f1_score_eval_fn, _flesch_kincaid_eval_fn, _mae_eval_fn, _mape_eval_fn, _max_error_eval_fn, _mse_eval_fn, _ndcg_at_k_eval_fn, _precision_at_k_eval_fn, _precision_eval_fn, _r2_score_eval_fn, _recall_at_k_eval_fn, _recall_eval_fn, _rmse_eval_fn, _rouge1_eval_fn, _rouge2_eval_fn, _rougeL_eval_fn, _rougeLsum_eval_fn, _token_count_eval_fn, _toxicity_eval_fn
  from mlflow.models import EvaluationMetric, make_metric
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 22 for calling a global function with example usage: f1_score() and returns: make_metric