code: def classification_metrics_multilabel(y_true, y_pred):
      metrics = {
          "Accuracy": sklm.accuracy_score,
          "Precision": lambda real, pred: sklm.precision_score(
              real, pred, average="weighted"
          ),
          "Recall": lambda real, pred: sklm.recall_score(
              real, pred, average="weighted"
          ),
          "F1": lambda real, pred: sklm.f1_score(
              real, pred, average="weighted"
          ),
      }
      return evaluate_metrics(y_true, y_pred, metrics)
  
file_path: hummingbird/benchmarks/trees/metrics.py
imports: import numpy
  import sklearn.metrics
  from benchmarks.datasets import LearningTask
label_desc: Use image node_id 4 for calling a global function with example usage: classification_metrics_multilabel(y_true, y_pred) and returns: evaluate_metrics