code: def get_valid_classes(X, label, threshold):
      class_counts = X[label].value_counts()
      class_counts_valid = class_counts[class_counts >= threshold]
      valid_classes = list(class_counts_valid.index)
      sum_prior = sum(class_counts)
      sum_after = sum(class_counts_valid)
      percent = sum_after / sum_prior
      if len(valid_classes) < len(class_counts):
          logger.log(
              25,
              "Warning: Some classes in the training set have fewer than %s examples. AutoGluon will only keep %s out of %s classes for training and will not try to predict the rare classes. "
              "To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold."
              % (threshold, len(valid_classes), len(class_counts)),
          )
      if percent < 1.0:
          logger.log(
              25,
              "Fraction of data from classes with at least %s examples that will be kept for training models: %s"
              % (threshold, percent),
          )
      return valid_classes
  
file_path: autogluon/core/src/autogluon/core/data/cleaner.py
imports: import logging
  from pandas import DataFrame
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION
label_desc: Use image node_id 4 for calling the CleanerMulticlass obj's underlying member method code with example usage: obj.get_valid_classes(X, label, threshold) and returns: valid_classes