class_docstr: Class that collects and stores information and statistics on issues found in a dataset.
  
  Parameters
  ----------
  data :
      The data object for which the issues are being collected.
  strategy :
      Strategy used for processing info dictionaries.
  
  Parameters
  ----------
  issues : pd.DataFrame
      Stores information about each individual issue found in the data,
      on a per-example basis.
  issue_summary : pd.DataFrame
      Summarizes the overall statistics for each issue type.
  info : dict
      A dictionary that contains information and statistics about the data and each issue type.
class_docstr_tok: ["Class","that","collects","and","stores","information","and","statistics","on","issues","found","in","a","dataset",".","Parameters","--","--","--","--","--","data",":","The","data","object","for","which","the","issues","are","being","collected",".","strategy",":","Strategy","used","for","processing","info","dictionaries",".","Parameters","--","--","--","--","--","issues",":","pd.DataFrame","Stores","information","about","each","individual","issue","found","in","the","data",",","on","a","per-example","basis",".","issue_summary",":","pd.DataFrame","Summarizes","the","overall","statistics","for","each","issue","type",".","info",":","dict","A","dictionary","that","contains","information","and","statistics","about","the","data","and","each","issue","type","."]
docstr: Collect results from Imagelab and update datalab.issues and datalab.issue_summary
  
  Parameters
  ----------
  imagelab: Imagelab
      Imagelab instance that run all the checks for image issue types
docstr_tok: ["Collect","results","from","Imagelab","and","update","datalab.issues","and","datalab.issue_summary","Parameters","--","--","--","--","--","imagelab",":","Imagelab","Imagelab","instance","that","run","all","the","checks","for","image","issue","types"]
code: def collect_issues_from_imagelab(
      self, imagelab: "Imagelab", issue_types: List[str]
  ) -> None:
      """
      Collect results from Imagelab and update datalab.issues and datalab.issue_summary
  
      Parameters
      ----------
      imagelab: Imagelab
          Imagelab instance that run all the checks for image issue types
      """
      overlapping_issues = list(
          set(self.issue_summary["issue_type"]) & set(issue_types)
      )
      self._update_issues_imagelab(imagelab, overlapping_issues)
  
      if overlapping_issues:
          warnings.warn(
              f"Overwriting {overlapping_issues} rows in self.issue_summary from imagelab."
          )
      self.issue_summary = self.issue_summary[
          ~self.issue_summary["issue_type"].isin(overlapping_issues)
      ]
      imagelab_summary_copy = imagelab.issue_summary.copy()
      imagelab_summary_copy = self.filter_based_on_max_prevalence(
          imagelab_summary_copy,
          int(IMAGELAB_ISSUES_MAX_PREVALENCE * len(self.issues)),
      )
  
      imagelab_summary_copy.rename(
          {"num_images": "num_issues"}, axis=1, inplace=True
      )
      self.issue_summary = pd.concat(
          [self.issue_summary, imagelab_summary_copy],
          axis=0,
          ignore_index=True,
      )
      for issue_type in issue_types:
          self._update_issue_info(issue_type, imagelab.info[issue_type])
  
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 4 for calling the ImagelabDataIssuesAdapter obj's underlying member method code with example usage: obj.collect_issues_from_imagelab(imagelab, issue_types) without return types