class_docstr: CleanLearning = Machine Learning with cleaned data (even when training on messy, error-ridden data).
  
  Automated and robust learning with noisy labels using any dataset and any model. This class
  trains a model `clf` with error-prone, noisy labels as if the model had been instead trained
  on a dataset with perfect labels. It achieves this by cleaning out the error and providing
  cleaned data while training. This class is currently intended for standard (multi-class) classification tasks.
  
  Parameters
  ----------
  clf : estimator instance, optional
    A classifier implementing the `sklearn estimator API
    <https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_,
    defining the following functions:
  
    * ``clf.fit(X, y, sample_weight=None)``
    * ``clf.predict_proba(X)``
    * ``clf.predict(X)``
    * ``clf.score(X, y, sample_weight=None)``
  
    See :py:mod:`cleanlab.experimental` for examples of sklearn wrappers,
    e.g. around PyTorch and FastText.
  
    If the model is not sklearn-compatible by default, it might be the case that
    standard packages can adapt the model. For example, you can adapt PyTorch
    models using `skorch <https://skorch.readthedocs.io/>`_ and adapt Keras models
    using `SciKeras <https://www.adriangb.com/scikeras/>`_.
  
    Stores the classifier used in Confident Learning.
    Default classifier used is `sklearn.linear_model.LogisticRegression
    <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`_.
    Default classifier assumes that indexing along the first dimension of the dataset corresponds to
    selecting different training examples.
  
  seed : int, optional
    Set the default state of the random number generator used to split
    the cross-validated folds. By default, uses `np.random` current random state.
  
  cv_n_folds : int, default=5
    This class needs holdout predicted probabilities for every data example
    and if not provided, uses cross-validation to compute them.
    `cv_n_folds` sets the number of cross-validation folds used to compute
    out-of-sample probabilities for each example in `X`.
  
  converge_latent_estimates : bool, optional
    If true, forces numerical consistency of latent estimates. Each is
    estimated independently, but they are related mathematically with closed
    form equivalences. This will iteratively enforce consistency.
  
  pulearning : {None, 0, 1}, default=None
    Only works for 2 class datasets. Set to the integer of the class that is
    perfectly labeled (you are certain that there are no errors in that class).
  
  find_label_issues_kwargs : dict, optional
    Keyword arguments to pass into :py:func:`filter.find_label_issues
    <cleanlab.filter.find_label_issues>`. Particularly useful options include:
    `filter_by`, `frac_noise`, `min_examples_per_class` (which all impact ML accuracy),
    `n_jobs` (set this to 1 to disable multi-processing if it's causing issues).
  
  label_quality_scores_kwargs : dict, optional
    Keyword arguments to pass into :py:func:`rank.get_label_quality_scores
    <cleanlab.rank.get_label_quality_scores>`. Options include: `method`, `adjust_pred_probs`.
  
  verbose : bool, default=False
    Controls how much output is printed. Set to ``False`` to suppress print
    statements.
  
  low_memory: bool, default=False
    Set as ``True`` if you have a big dataset with limited memory.
    Uses :py:func:`experimental.label_issues_batched.find_label_issues_batched <cleanlab.experimental.label_issues_batched>`
    to find label issues.
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docstr: Predict class labels using your wrapped classifier `clf`.
  Works just like ``clf.predict()``.
  
  Parameters
  ----------
  X : np.ndarray or DatasetLike
    Test data in the same format expected by your wrapped classifier.
  
  Returns
  -------
  class_predictions : np.ndarray
    Vector of class predictions for the test examples.
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code: def predict(self, *args, **kwargs) -> np.ndarray:
      """Predict class labels using your wrapped classifier `clf`.
      Works just like ``clf.predict()``.
  
      Parameters
      ----------
      X : np.ndarray or DatasetLike
        Test data in the same format expected by your wrapped classifier.
  
      Returns
      -------
      class_predictions : np.ndarray
        Vector of class predictions for the test examples.
      """
      if self._default_clf:
          if args:
              X = args[0]
          elif "X" in kwargs:
              X = kwargs["X"]
              del kwargs["X"]
          else:
              raise ValueError(
                  "No input provided to predict, please provide X."
              )
          X = force_two_dimensions(X)
          new_args = (X,) + args[1:]
          return self.clf.predict(*new_args, **kwargs)
      else:
          return self.clf.predict(*args, **kwargs)
  
imports: from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score
  from sklearn.base import BaseEstimator
  import numpy
  import pandas
  import inspect
  import warnings
  from typing import Optional, TYPE_CHECKING
  from cleanlab.rank import get_label_quality_scores
  from cleanlab import filter
  from cleanlab.internal.util import value_counts, compress_int_array, subset_X_y, get_num_classes, force_two_dimensions
  from cleanlab.count import estimate_py_noise_matrices_and_cv_pred_proba, estimate_py_and_noise_matrices_from_probabilities, estimate_cv_predicted_probabilities, estimate_latent, compute_confident_joint
  from cleanlab.internal.latent_algebra import compute_py_inv_noise_matrix, compute_noise_matrix_from_inverse
  from cleanlab.internal.validation import assert_valid_inputs, labels_to_array
  from cleanlab.experimental.label_issues_batched import find_label_issues_batched
label_desc: Use image node_id 3 for calling the CleanLearning obj's underlying member method code with example usage: obj.predict() and returns: self, self