class_docstr: Manages issues related to null/missing values in the rows of features.
  
  Parameters
  ----------
  datalab :
      The Datalab instance that this issue manager searches for issues in.
class_docstr_tok: ["Manages","issues","related","to","null\/missing","values","in","the","rows","of","features",".","Parameters","--","--","--","--","--","datalab",":","The","Datalab","instance","that","this","issue","manager","searches","for","issues","in","."]
code: def collect_info(self, null_tracker: np.ndarray) -> dict:
      most_common_issue = self._most_common_issue(
          null_tracker=null_tracker
      )
      column_impact = self._column_impact(null_tracker=null_tracker)
      average_null_score = {
          "average_null_score": self.issues[self.issue_score_key].mean()
      }
      issues_dict = {
          **average_null_score,
          **most_common_issue,
          **column_impact,
      }
      info_dict: Dict[str, Any] = {**issues_dict}
      return info_dict
  
file_path: cleanlab/cleanlab/datalab/internal/issue_manager/null.py
imports: from __future__ import annotations
  from collections import Counter
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, List
  import numpy
  import pandas
  from numpy import ndarray
  from cleanlab.datalab.internal.issue_manager import IssueManager
label_desc: Use image node_id 5 for calling the NullIssueManager obj's underlying member method code with example usage: obj.collect_info(null_tracker) and returns: info_dict