code: def _get_annotator_agreement_with_annotators(
      labels_multiannotator: np.ndarray,
      num_annotations: np.ndarray,
      verbose: bool = True,
  ) -> np.ndarray:
      """Returns the average agreement of each annotator with other annotators that label the same example.
  
      Parameters
      ----------
      labels_multiannotator : np.ndarray
          2D numpy array 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.
      verbose : bool, default = True
          Certain warnings and notes will be printed if ``verbose`` is set to ``True``.
  
      Returns
      -------
      annotator_agreement : np.ndarray
          An array of shape ``(M,)`` where M is the number of annotators, with the agreement of each annotator with other
          annotators that labeled the same examples.
      """
  
      annotator_agreement_with_annotators = np.zeros(
          labels_multiannotator.shape[1]
      )
      for i in range(len(annotator_agreement_with_annotators)):
          annotator_labels = labels_multiannotator[:, i]
          annotator_labels_mask = ~np.isnan(annotator_labels)
          annotator_agreement_with_annotators[
              i
          ] = _get_single_annotator_agreement(
              labels_multiannotator[annotator_labels_mask],
              num_annotations[annotator_labels_mask],
              i,
          )
  
      # impute average annotator accuracy for any annotator that do not overlap with other annotators
      non_overlap_mask = np.isnan(annotator_agreement_with_annotators)
      if np.sum(non_overlap_mask) > 0:
          if verbose:
              print(
                  f"Annotator(s) {list(np.where(non_overlap_mask)[0])} did not annotate any examples that overlap with other annotators, \
                  \nusing the average annotator agreeement among other annotators as this annotator's agreement."
              )
  
          avg_annotator_agreement = np.mean(
              annotator_agreement_with_annotators[~non_overlap_mask]
          )
          annotator_agreement_with_annotators[
              non_overlap_mask
          ] = avg_annotator_agreement
  
      return annotator_agreement_with_annotators
  
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 10 for calling a global function with example usage: _get_annotator_agreement_with_annotators(labels_multiannotator, num_annotations, verbose) and returns: annotator_agreement_with_annotators