code: def check_consensus_label_classes(
      labels_multiannotator: np.ndarray,
      consensus_label: np.ndarray,
      consensus_method: str,
  ) -> None:
      """Check if any classes no longer appear in the set of consensus labels (established using the consensus_method stated)"""
      unique_ma_labels = np.unique(labels_multiannotator)
      unique_ma_labels = unique_ma_labels[~np.isnan(unique_ma_labels)]
      labels_set_difference = set(unique_ma_labels) - set(
          consensus_label
      )
  
      if len(labels_set_difference) > 0:
          print(
              "CAUTION: Number of unique classes has been reduced from the original data when establishing consensus labels "
              f"using consensus method '{consensus_method}', likely due to some classes being rarely annotated. "
              "If training a classifier on these consensus labels, it will never see any of the omitted classes unless you "
              "manually replace some of the consensus labels.\n"
              f"Classes in the original data but not in consensus labels: {list(map(int, labels_set_difference))}"
          )
  
file_path: cleanlab/cleanlab/internal/multiannotator_utils.py
imports: from cleanlab.typing import LabelLike
  from typing import Optional, Tuple
  import warnings
  import numpy
  import pandas
  from cleanlab.internal.validation import assert_valid_class_labels
  from cleanlab.internal.numerics import softmax
  from cleanlab.internal.util import get_num_classes, value_counts
label_desc: Use image node_id 4 for calling a global function with example usage: check_consensus_label_classes(labels_multiannotator, consensus_label, consensus_method) without return types