code: def get_json_node(
      arg,
      condition,
      error,
      nodeid,
      method,
      node_name,
      parentid,
      p_node_name,
      total,
      success,
      metric_name,
      metric_value,
      is_error_metric,
      is_regression_metric,
  ):
      """Get the JSON node for the tree.
  
      :param arg: The arg for the node.
      :type arg: str
      :param condition: The condition for the node.
      :type condition: str
      :param error: The error for the node.
          Can be int for classification or float for regression.
      :type error: int or float
      :param nodeid: The node id for the node.
      :type nodeid: int
      :param method: The method for the node.
      :type method: str
      :param node_name: The node name for the node.
      :type node_name: str
      :param parentid: The parent id for the node.
      :type parentid: int
      :param p_node_name: The parent node name for the node.
      :type p_node_name: str
      :param total: The total number of instances in the node.
      :type total: int
      :param success: The total number of success instances for the node.
      :type success: int
      :param metric_name: The metric name for the node.
      :type metric_name: str
      :param metric_value: The metric value for the node.
      :type metric_value: float
      :param is_error_metric: Whether the metric is an error metric.
      :type is_error_metric: bool
      :param is_regression_metric: Whether the metric is a regression metric.
      :type is_regression_metric: bool
      :return: The JSON node.
      :rtype: dict
      """
      if is_regression_metric:
          error = float(error)
      else:
          error = int(error)
      return {
          "arg": arg,
          "badFeaturesRowCount": 0,  # Note: remove this eventually
          "condition": condition,
          "error": error,
          "id": int(nodeid),
          METHOD: method,
          "nodeIndex": int(nodeid),
          "nodeName": node_name,
          "parentId": parentid,
          "parentNodeName": p_node_name,
          "pathFromRoot": "",  # Note: remove this eventually
          "size": float(total),
          "sourceRowKeyHash": "hashkey",  # Note: remove this eventually
          "success": float(success),  # Note: remove this eventually
          TreeNode.METRIC_NAME: metric_name,
          TreeNode.METRIC_VALUE: float(metric_value),
          "isErrorMetric": is_error_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 20 for calling a global function with example usage: get_json_node(arg, condition, error, nodeid, method, node_name, parentid, p_node_name, total, success, metric_name, metric_value, is_error_metric, is_regression_metric) and returns: dict