code: def get_missing_classes(
      labels, *, pred_probs=None, num_classes=None, multi_label=False
  ):
      """Find which classes are present in ``pred_probs`` but not present in ``labels``.
  
      See ``count.compute_confident_joint`` for parameter docstrings."""
      if pred_probs is None and num_classes is None:
          raise ValueError(
              "Both pred_probs and num_classes are None. You must provide exactly one."
          )
      if pred_probs is not None and num_classes is not None:
          raise ValueError(
              "Both pred_probs and num_classes are not None. Only one may be provided."
          )
      if num_classes is None:
          num_classes = pred_probs.shape[1]
      unique_classes = get_unique_classes(
          labels, multi_label=multi_label
      )
      return sorted(set(range(num_classes)).difference(unique_classes))
  
imports: import warnings
  import numpy
  import pandas
  from typing import Union, Tuple
  from cleanlab.typing import DatasetLike, LabelLike
  from cleanlab.internal.validation import labels_to_array
  from cleanlab.internal.constants import FLOATING_POINT_COMPARISON, TINY_VALUE
label_desc: Use image node_id 6 for calling a global function with example usage: get_missing_classes(labels) and returns: sorted