code: def _accuracy_eval_fn(
      predictions, targets=None, metrics=None, sample_weight=None
  ):
      if targets is not None and len(targets) != 0:
          from sklearn.metrics import accuracy_score
  
          acc = accuracy_score(
              y_true=targets,
              y_pred=predictions,
              sample_weight=sample_weight,
          )
          return MetricValue(aggregate_results={"exact_match": acc})
  
imports: import functools
  import logging
  import os
  import numpy
  from mlflow.metrics.base import MetricValue, standard_aggregations
label_desc: Use image node_id 9 for calling a global function with example usage: _accuracy_eval_fn(predictions, targets, metrics, sample_weight) and returns: MetricValue