class_docstr: A single object to automatically detect all kinds of issues in datasets.
  This is how we recommend you interface with the cleanlab library if you want to audit the quality of your data and detect issues within it.
  If you have other specific goals (or are doing a less standard ML task not supported by Datalab), then consider using the other methods across the library.
  Datalab tracks intermediate state (e.g. data statistics) from certain cleanlab functions that can be re-used across other cleanlab functions for better efficiency.
  
  Parameters
  ----------
  data : Union[Dataset, pd.DataFrame, dict, list, str]
      Dataset-like object that can be converted to a Hugging Face Dataset object.
  
      It should contain the labels for all examples, identified by a
      `label_name` column in the Dataset object.
  
      Supported formats:
        - datasets.Dataset
        - pandas.DataFrame
        - dict (keys are strings, values are arrays/lists of length ``N``)
        - list (list of dictionaries that each have the same keys)
        - str
  
          - path to a local file: Text (.txt), CSV (.csv), JSON (.json)
          - or a dataset identifier on the Hugging Face Hub
  
  task : str
      The type of machine learning task that the dataset is used for.
      By default, this is set to "classification", but you can also set it to "regression" if you are working with a regression dataset.
  
  label_name : str, optional
      The name of the label column in the dataset.
  
  image_key : str, optional
      Optional key that can be specified for image datasets to point to the field containing the actual images themselves.
      If specified, additional image-specific issue types can be detected in the dataset.
      See the CleanVision package `documentation <https://cleanvision.readthedocs.io/en/latest/>`_ for descriptions of these image-specific issue types.
  
  verbosity : int, optional
      The higher the verbosity level, the more information
      Datalab prints when auditing a dataset.
      Valid values are 0 through 4. Default is 1.
  
  Examples
  --------
  >>> import datasets
  >>> from cleanlab import Datalab
  >>> data = datasets.load_dataset("glue", "sst2", split="train")
  >>> datalab = Datalab(data, label_name="label")
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code: def __repr__(self) -> str:
      return _Displayer(data_issues=self.data_issues).__repr__()
  
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import numpy
  import pandas
  import cleanlab
  from cleanlab.datalab.internal.adapter.imagelab import create_imagelab
  from cleanlab.datalab.internal.data import Data
  from cleanlab.datalab.internal.display import _Displayer
  from cleanlab.datalab.internal.helper_factory import _DataIssuesBuilder, issue_finder_factory, report_factory
  from cleanlab.datalab.internal.issue_manager_factory import list_default_issue_types, list_possible_issue_types
  from cleanlab.datalab.internal.serialize import _Serializer
label_desc: Use image node_id 2 for calling the Datalab obj's underlying member method code with example usage: obj.__repr__() and returns: _Displayer