code: def find_issues(
      self,
      *,
      pred_probs: Optional[np.ndarray] = None,
      features: Optional[npt.NDArray] = None,
      knn_graph: Optional[csr_matrix] = None,
      issue_types: Optional[Dict[str, Any]] = None,
  ) -> None:
      datalab_issue_types = (
          {
              k: v
              for k, v in issue_types.items()
              if k != "image_issue_types"
          }
          if issue_types
          else issue_types
      )
      super().find_issues(
          pred_probs=pred_probs,
          features=features,
          knn_graph=knn_graph,
          issue_types=datalab_issue_types,
      )
  
      issue_types_copy = self._get_imagelab_issue_types(issue_types)
      if not issue_types_copy:
          return
      try:
          if self.verbosity:
              print(
                  f'Finding {", ".join(issue_types_copy.keys())} images ...'
              )
  
          self.imagelab.find_issues(
              issue_types=issue_types_copy, verbose=False
          )
  
          self.datalab.data_issues.collect_statistics(self.imagelab)
          self.datalab.data_issues.collect_issues_from_imagelab(
              self.imagelab, issue_types_copy.keys()
          )
      except Exception as e:
          print(f"Error in checking for image issues: {e}")
  
imports: import warnings
  from typing import TYPE_CHECKING, Any, Dict, List, Optional
  import numpy
  import numpy.typing
  import pandas
  from scipy.sparse import csr_matrix
  from cleanlab.datalab.internal.adapter.constants import DEFAULT_CLEANVISION_ISSUES, IMAGELAB_ISSUES_MAX_PREVALENCE
  from cleanlab.datalab.internal.data import Data
  from cleanlab.datalab.internal.data_issues import DataIssues, _InfoStrategy
  from cleanlab.datalab.internal.issue_finder import IssueFinder
  from cleanlab.datalab.internal.report import Reporter
label_desc: Use image node_id 3 for calling the ImagelabIssueFinderAdapter obj's underlying member method code with example usage: obj.find_issues() without return types