code: def _mae_eval_fn(
      predictions, targets=None, metrics=None, sample_weight=None
  ):
      if targets is not None and len(targets) != 0:
          from sklearn.metrics import mean_absolute_error
  
          mae = mean_absolute_error(
              targets, predictions, sample_weight=sample_weight
          )
          return MetricValue(
              aggregate_results={"mean_absolute_error": mae}
          )
  
imports: import functools
  import logging
  import os
  import numpy
  from mlflow.metrics.base import MetricValue, standard_aggregations
label_desc: Use image node_id 14 for calling a global function with example usage: _mae_eval_fn(predictions, targets, metrics, sample_weight) and returns: MetricValue