code: def filter_by_class(
      class_index: int,
      issues: np.ndarray,
      labels: np.ndarray,
      pred_probs: np.ndarray,
  ) -> np.ndarray:
      """
      Return label issues involving particular class. Note that this includes errors where the given label is the class of interest, and the predicted label is any other class.
  
      Parameters
      ----------
      class_index:
        The specific class you are interested in.
  
      issues:
        Boolean **mask** for the entire dataset where ``True`` represents a pixel label issue and ``False`` represents an example that is
        accurately labeled.
  
        Same format as output by :py:func:`segmentation.filter.find_label_issues <cleanlab.segmentation.filter.find_label_issues>`
        or :py:func:`segmentation.rank.issues_from_scores <cleanlab.segmentation.rank.issues_from_scores>`.
  
      labels:
        A discrete array of noisy labels for a segmantic segmentation dataset, in the shape ``(N,H,W,)``,
        where each pixel must be integer in 0, 1, ..., K-1.
        Refer to documentation for this argument in :py:func:`find_label_issues <cleanlab.segmentation.filter.find_label_issues>` for further details.
  
      pred_probs:
        An array of shape ``(N,K,H,W,)`` of model-predicted class probabilities.
        Refer to documentation for this argument in :py:func:`find_label_issues <cleanlab.segmentation.filter.find_label_issues>` for further details.
  
      Returns
      ----------
      issues_subset:
        Boolean **mask** for the subset dataset where ``True`` represents a pixel label issue and ``False`` represents an example that is
        accurately labeled for the labeled class.
  
        Returned mask shows **all** instances that involve the particular class of interest.
  
  
      """
      issues_subset = (issues & np.isin(labels, class_index)) | (
          issues & np.isin(pred_probs.argmax(1), class_index)
      )
      return issues_subset
  
file_path: cleanlab/cleanlab/segmentation/summary.py
imports: from typing import Any, Dict, List, Optional
  import numpy
  import pandas
  from tqdm import tqdm
  from cleanlab.internal.segmentation_utils import _get_summary_optional_params
label_desc: Use image node_id 3 for calling a global function with example usage: filter_by_class(class_index, issues, labels, pred_probs) and returns: issues_subset