code: def bin_data(df, feat, bins, quantile_binning=False):
      """Bins the input data for the specified feature and binning method.
  
      Uses equal width, quantile binning or specified custom bins. Custom
      bins is used when bins is an IntervalIndex instead of a constant number.
  
      :param df: The pandas Series to bin.
      :type df: pd.Series
      :param feat: The feature name to bin.
      :type feat: str
      :param bins: The number of bins or the specified bins.
      :type bins: int or pd.IntervalIndex
      :param quantile_binning: Whether to use quantile binning.
      :type quantile_binning: bool
      :returns: The binned DataFrame.
      :rtype: pd.DataFrame
      """
      feat_col = df[feat]
      # Note: if column is empty pd.qcut raises error but pd.cut does not
      # and just returns the correct binned data and bins if retbins=True.
      # If using existing array of bins we manually compute the bins
      # so the same categories are used.
      is_bins_constant = not isinstance(bins, pd.IntervalIndex)
      if not is_bins_constant:
          if feat_col.empty:
              bin_indexes = np.array([])
          else:
              bin_indexes = bins.get_indexer(np.array(feat_col))
          cat = pd.Categorical.from_codes(
              bin_indexes, categories=bins, ordered=True
          )
          cat_typed = feat_col._constructor(
              cat, index=feat_col.index, name=feat_col.name
          )
          return cat_typed
      if quantile_binning and not feat_col.empty:
          bindf = pd.qcut(
              feat_col, bins, precision=PRECISION, duplicates=DROP
          )
          zero_interval = bindf.cat.categories[0]
          left_abs = abs(zero_interval.left)
          left = zero_interval.left - ZERO_BIN_TOL * left_abs
          zero_interval = pd.Interval(
              left=left,
              right=zero_interval.right,
              closed=zero_interval.closed,
          )
          indexes = [zero_interval]
          for idx in range(1, len(bindf.cat.categories)):
              indexes.append(bindf.cat.categories[idx])
          cats = pd.IntervalIndex(
              indexes,
              closed=zero_interval.closed,
              dtype=bindf.cat.categories.dtype,
              name=bindf.cat.categories.name,
          )
          bindf.cat.categories = cats
          # re-bin data according to new categories, otherwise can have
          # issues with precision when using pd.qcut.
          # Specifically, it can bin some points incorrectly for low precision,
          # for example a point with value -0.0127796318808497
          # will be binned incorrectly in interval
          # (-0.0309423241359475, -0.012779631880849702]
          # even though it should be binned in the interval above since
          # -0.0127796318808497 > -0.012779631880849702
          bindf = bin_data(
              df, feat, bindf.cat.categories, quantile_binning
          )
          return bindf
      else:
          cut_df = pd.cut(feat_col, bins, precision=PRECISION)
          return adjust_bin_max_edge(cut_df, feat_col)
  
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 6 for calling a global function with example usage: bin_data(df, feat, bins, quantile_binning) and returns: cat_typed, bindf, adjust_bin_max_edge