code: def _rmse_eval_fn(
      predictions, targets=None, metrics=None, sample_weight=None
  ):
      if targets is not None and len(targets) != 0:
          from sklearn.metrics import mean_squared_error
  
          rmse = mean_squared_error(
              targets,
              predictions,
              squared=False,
              sample_weight=sample_weight,
          )
          return MetricValue(
              aggregate_results={"root_mean_squared_error": rmse}
          )
  
imports: import functools
  import logging
  import os
  import numpy
  from mlflow.metrics.base import MetricValue, standard_aggregations
label_desc: Use image node_id 16 for calling a global function with example usage: _rmse_eval_fn(predictions, targets, metrics, sample_weight) and returns: MetricValue