code: def create_categorical_query(
      method, arg, p_node_name, p_node_query, parent, categories
  ):
      """Create the categorical query for given method and argument.
  
      :param method: The method to use for the categorical query.
      :type method: str
      :param arg: The argument to use for the categorical query.
      :type arg: list[float]
      :param p_node_name: The name of the node.
      :type p_node_name: str
      :param p_node_query: The reference to the node to be used in the query.
      :type p_node_query: str
      :param parent: The parent node.
      :type parent: dict
      :param categories: The list of categories for the current node.
      :type categories: list[tuple]
      :return: The categorical query and condition.
      :rtype: tuple(str, str)
      """
      if method == CohortFilterMethods.METHOD_INCLUDES:
          operation = "=="
      else:
          operation = "!="
      categorical_values = categories[0]
      categorical_indexes = categories[1]
      thresholds = []
      catcoli = categorical_indexes.index(parent[SPLIT_FEATURE])
      catvals = categorical_values[catcoli]
      for argi in arg:
          encoded_val = catvals[int(argi)]
          if not isinstance(encoded_val, str):
              encoded_val = str(encoded_val)
          thresholds.append(encoded_val)
      threshold_str = " | ".join(thresholds)
      condition = "{} {} {}".format(
          p_node_name, operation, threshold_str
      )
      query = []
      for argi in arg:
          query.append(p_node_query + " " + operation + " " + str(argi))
      if method == CohortFilterMethods.METHOD_INCLUDES:
          query = " | ".join(query)
      else:
          query = " & ".join(query)
      return query, condition
  
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 14 for calling a global function with example usage: create_categorical_query(method, arg, p_node_name, p_node_query, parent, categories) and returns: query,  condition