code: def _adjust_threshold_if_necessary(
      self, y, threshold, holdout_frac, num_bag_folds
  ):
      new_threshold = threshold
      num_rows = len(y)
      holdout_frac = max(holdout_frac, 1 / num_rows + 0.001)
      num_bag_folds = min(num_bag_folds, num_rows)
  
      if num_bag_folds < 2:
          minimum_safe_threshold = 1
      else:
          minimum_safe_threshold = 2
  
      if minimum_safe_threshold > new_threshold:
          new_threshold = minimum_safe_threshold
  
      if self.problem_type in [REGRESSION, QUANTILE]:
          return new_threshold, holdout_frac, num_bag_folds
  
      class_counts = y.value_counts()
      total_rows = class_counts.sum()
      minimum_percent_to_keep = 0.975
      minimum_rows_to_keep = math.ceil(
          total_rows * minimum_percent_to_keep
      )
      minimum_class_to_keep = 2
  
      num_classes = len(class_counts)
      class_counts_valid = class_counts[class_counts >= new_threshold]
      num_rows_valid = class_counts_valid.sum()
      num_classes_valid = len(class_counts_valid)
  
      if (num_rows_valid >= minimum_rows_to_keep) and (
          num_classes_valid >= minimum_class_to_keep
      ):
          return new_threshold, holdout_frac, num_bag_folds
  
      num_classes_valid = 0
      num_rows_valid = 0
      new_threshold = None
      for i in range(num_classes):
          num_classes_valid += 1
          num_rows_valid += class_counts.iloc[i]
          new_threshold = class_counts.iloc[i]
          if (num_rows_valid >= minimum_rows_to_keep) and (
              num_classes_valid >= minimum_class_to_keep
          ):
              break
  
      return new_threshold, holdout_frac, num_bag_folds
  
imports: import copy
  import logging
  import math
  import time
  import numpy
  import pandas
  from pandas import DataFrame
  from autogluon.common.utils.log_utils import convert_time_in_s_to_log_friendly
  from autogluon.common.utils.system_info import get_ag_system_info
  from autogluon.core.constants import AUTO_WEIGHT, BALANCE_WEIGHT, BINARY, MULTICLASS, QUANTILE, REGRESSION
  from autogluon.core.data import LabelCleaner
  from autogluon.core.data.cleaner import Cleaner
  from autogluon.core.utils.time import sample_df_for_time_func, time_func
  from autogluon.core.utils.utils import augment_rare_classes, extract_column
  from ..trainer import AutoTrainer
  from .abstract_learner import AbstractTabularLearner
label_desc: Use image node_id 8 for calling the DefaultLearner obj's underlying member method code with example usage: obj._adjust_threshold_if_necessary(y, threshold, holdout_frac, num_bag_folds) and returns: new_threshold,  holdout_frac,  num_bag_folds, new_threshold,  holdout_frac,  num_bag_folds, new_threshold,  holdout_frac,  num_bag_folds