code: def _max_error_eval_fn(predictions, targets=None, metrics=None):
      if targets is not None and len(targets) != 0:
          from sklearn.metrics import max_error
  
          error = max_error(targets, predictions)
          return MetricValue(aggregate_results={"max_error": error})
  
imports: import functools
  import logging
  import os
  import numpy
  from mlflow.metrics.base import MetricValue, standard_aggregations
label_desc: Use image node_id 18 for calling a global function with example usage: _max_error_eval_fn(predictions, targets, metrics) and returns: MetricValue