code: def _get_annotator_worst_class(
      labels_multiannotator: np.ndarray,
      consensus_label: np.ndarray,
      consensus_quality_score: np.ndarray,
  ) -> np.ndarray:
      """Returns the class which each annotator makes the most errors in.
  
      Parameters
      ----------
      labels_multiannotator : np.ndarray
          2D pandas DataFrame of multiple given labels for each example with shape ``(N, M)``,
          where N is the number of examples and M is the number of annotators.
          For more details, labels in the same format expected by the `~cleanlab.multiannotator.get_label_quality_multiannotator`.
      consensus_label : np.ndarray
          An array of shape ``(N,)`` with the consensus labels aggregated from all annotators.
      consensus_quality_score : np.ndarray
          An array of shape ``(N,)`` with the quality score of the consensus.
  
      Returns
      -------
      worst_class : np.ndarray
          The class that is most frequently mislabeled by a given annotator.
      """
  
      worst_class = np.apply_along_axis(
          _get_single_annotator_worst_class,
          axis=0,
          arr=labels_multiannotator,
          consensus_label=consensus_label,
          consensus_quality_score=consensus_quality_score,
      ).astype(int)
  
      return worst_class
  
imports: import warnings
  import numpy
  import pandas
  from typing import List, Dict, Any, Union, Tuple, Optional
  from cleanlab.rank import get_label_quality_scores
  from cleanlab.internal.util import get_num_classes, value_counts
  from cleanlab.internal.constants import CLIPPING_LOWER_BOUND
  from cleanlab.internal.multiannotator_utils import assert_valid_inputs_multiannotator, assert_valid_pred_probs, check_consensus_label_classes, find_best_temp_scaler, temp_scale_pred_probs
label_desc: Use image node_id 17 for calling a global function with example usage: _get_annotator_worst_class(labels_multiannotator, consensus_label, consensus_quality_score) and returns: worst_class