class_docstr: The IssueFinder class is responsible for managing the process of identifying
  issues in the dataset by handling the creation and execution of relevant
  IssueManagers. It serves as a coordinator or helper class for the Datalab class
  to encapsulate the specific behavior of the issue finding process.
  
  At a high level, the IssueFinder is responsible for:
  
  - Determining which types of issues to look for.
  - Instantiating the appropriate IssueManagers using a factory.
  - Running the IssueManagers' `find_issues` methods.
  - Collecting the results into a DataIssues instance.
  
  Parameters
  ----------
  datalab : Datalab
      The Datalab instance associated with this IssueFinder.
  
  task : str
      The type of machine learning task that the dataset is used for.
  
  verbosity : int
      Controls the verbosity of the output during the issue finding process.
  
  Note
  ----
  This class is not intended to be used directly. Instead, use the
  `Datalab.find_issues` method which internally utilizes an IssueFinder instance.
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code: def __init__(self, datalab: "Datalab", task: str, verbosity=1):
      self.datalab = datalab
      self.task = task
      self.verbosity = verbosity
  
file_path: cleanlab/cleanlab/datalab/internal/issue_finder.py
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, Optional
  import numpy
  from scipy.sparse import csr_matrix
  from cleanlab.datalab.internal.issue_manager_factory import _IssueManagerFactory, list_default_issue_types
  from cleanlab.datalab.internal.model_outputs import MultiClassPredProbs, RegressionPredictions
label_desc: Use image node_id 1 to create a new IssueFinder object  with example: obj = IssueFinder(datalab, task, verbosity)