code: def adjust_bin_max_edge(cut_df, feat_col):
      """Adjusts the max bin edge to account for floating point precision.
  
      Adjustment only done when the max bin edge is less than the max value.
  
      :param cut_df: The binned DataFrame.
      :type cut_df: pd.DataFrame
      :param feat_col: The feature column to adjust the bins for.
      :type feat_col: pd.Series
      """
      bins = cut_df.cat.categories
      max_bin = bins[-1]
      max_val = feat_col.max()
      if max_bin.right < max_val:
          adj_cat = bins.copy()
          max_bin = pd.Interval(
              left=max_bin.left, right=max_val, closed=max_bin.closed
          )
          adj_cat = adj_cat[:-1].append(pd.IntervalIndex([max_bin]))
          cut_df = cut_df.cat.rename_categories(adj_cat)
      return cut_df
  
imports: import logging
  import math
  import warnings
  from abc import ABC, abstractmethod
  import numpy
  import pandas
  from sklearn.metrics import multilabel_confusion_matrix
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import DIFF, PRED_Y, ROW_INDEX, TRUE_Y, MatrixParams, MetricKeys, Metrics, ModelTask, metric_to_display_name
  from erroranalysis._internal.metrics import get_ordered_classes, is_multi_agg_metric, metric_to_func
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 7 for calling a global function with example usage: adjust_bin_max_edge(cut_df, feat_col) and returns: cut_df