code: def create_empty_node(metric):
      """Create an empty node for the tree.
  
      :param metric: The metric to use for the node.
      :type metric: str
      :return: The empty node.
      :rtype: dict
      """
      metric_name = metric_to_display_name[metric]
      is_regression_metric = metric in regression_metrics
      is_error_metric = metric in error_metrics
      return [
          get_json_node(
              None,
              None,
              0,
              0,
              None,
              None,
              None,
              None,
              0,
              0,
              metric_name,
              0,
              is_error_metric,
              is_regression_metric,
          )
      ]
  
imports: import logging
  import numbers
  from enum import Enum
  import numpy
  import pandas
  from lightgbm import Booster, LGBMClassifier, LGBMRegressor
  from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error, r2_score
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import DIFF, LEAF_INDEX, METHOD, PRED_Y, ROW_INDEX, SPLIT_FEATURE, SPLIT_INDEX, TRUE_Y, CohortFilterMethods, Metrics, ModelTask, TreeNode, error_metrics, f1_metrics, metric_to_display_name, precision_metrics, recall_metrics, regression_metrics
  from erroranalysis._internal.metrics import get_ordered_classes, metric_to_func
  from erroranalysis._internal.process_categoricals import process_categoricals
  from erroranalysis._internal.utils import is_spark
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 19 for calling a global function with example usage: create_empty_node(metric) and returns: list