code: def convert_multi_label_to_multi_class(
      predictions: np.ndarray, model_classes: List[str]
  ) -> np.ndarray:
      """Convert multi-label predictions to multi class format like predictions."""
      predictions = np.asarray(predictions)
      samples_per_class = np.nansum(
          np.where(predictions is None, np.nan, predictions), axis=0
      )  # Ignoring nan values
      all_predictions = [
          [cls] * int(num_samples)
          for cls, num_samples in zip(model_classes, samples_per_class)
      ]
      return np.asarray(
          [item for sublist in all_predictions for item in sublist]
      ).reshape((-1, 1))
  
imports: import copy
  import warnings
  from collections import Counter
  from typing import List, Tuple, Union
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import MinMaxScaler
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.utils.distribution.rare_category_encoder import RareCategoryEncoder
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling a global function with example usage: convert_multi_label_to_multi_class(predictions, model_classes) and returns: np