code: def balance(
      df: pd.DataFrame,
      *cross_cols: str,
      upsample: float = 0.5,
      downsample: int = 1,
      bins: int = 6,
      categorical_cols: List[str] = [],
      plot: bool = True,
  ):
      """
      Balances a dataframe based on the columns and cross columns provided.
          The results can be upsampled or downsampled. By default, there is no
          downsample, and the upsample is towards a minimum of 50% of the
          frequency of the highest class.
  
      :Example:
  
      cat_df = xai.balance(
          df,
          "gender", "loan",
          upsample=0.8,
          downsample=0.8)
  
      :param df: Pandas Dataframe containing data (inputs and target )
      :type df: pandas.DataFrame
      :param *cross_cols: One or more positional arguments (passed as *args) that
      are used to split the data into the cross product of their values
      :type cross_cols: List[str]
      :param upsample: [Default: 0.5] Target upsample for columns lower
          than percentage.
      :type upsample: float
      :param downsample: [Default: 1] Target downsample for columns higher
          than percentage.
      :type downsample: float
      :param bins: [Default: 6] Number of bins to be used for numerical cols
      :type bins: int
      :param categorical_cols: [Default: []] Columns within dataframe that are
          categorical. Columns that are not np.objects and are not part explicitly
          provided here will be treated as numeric, and bins will be used.
      :type categorical_cols: List[str]
      :param threshold: [Default: 0.5] Threshold to display in the chart.
      :type threshold: float
      :returns: Dataframe with categorical numerical values.
      :rtype: pandas.DataFrame
  
      """
  
      if not len(categorical_cols):
          categorical_cols = df.select_dtypes(
              include=[np.object, np.bool]
          ).columns
  
      grouped = group_by_columns(
          df,
          list(cross_cols),
          bins=bins,
          categorical_cols=categorical_cols,
      )
  
      count_grp = grouped.count()
      count_max = count_grp.values.max()
      count_upsample = int(upsample * count_max)
      count_downsample = int(downsample * count_max)
  
      def norm(x):
          if x.shape[0] < count_upsample:
              return x.sample(count_upsample, replace=True)
          elif x.shape[0] > count_downsample:
              return x.sample(count_downsample)
          else:
              return x
  
      tmp_df = grouped.apply(norm).reset_index(drop=True)
  
      if plot:
          imbalance_plot(
              tmp_df,
              *cross_cols,
              bins=bins,
              categorical_cols=categorical_cols,
          )
  
      return tmp_df
  
imports: import pandas
  import matplotlib.pyplot
  import numpy
  from scipy.stats import spearmanr
  from scipy.cluster import hierarchy
  from typing import List, Any, Union, Tuple, Optional, Dict
  import random, math
  from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, confusion_matrix
  import itertools
  import logging
label_desc: Use image node_id 5 for calling a global function with example usage: balance(df) and returns: tmp_df, x, x, x