code: def _rougeLsum_eval_fn(predictions, targets=None, metrics=None):
      if not _validate_text_data(
          targets, "rougeLsum", targets_col_specifier
      ) or not _validate_text_data(
          predictions, "rougeLsum", predictions_col_specifier
      ):
          return
  
      try:
          rouge = _cached_evaluate_load("rouge")
      except Exception as e:
          _logger.warning(
              f"Failed to load 'rouge' metric (error: {e!r}), skipping metric logging."
          )
          return
  
      scores = rouge.compute(
          predictions=predictions,
          references=targets,
          rouge_types=["rougeLsum"],
          use_aggregator=False,
      )["rougeLsum"]
      return MetricValue(
          scores=scores,
          aggregate_results=standard_aggregations(scores),
      )
  
imports: import functools
  import logging
  import os
  import numpy
  from mlflow.metrics.base import MetricValue, standard_aggregations
label_desc: Use image node_id 13 for calling a global function with example usage: _rougeLsum_eval_fn(predictions, targets, metrics) and returns: MetricValue