code: def _transform_to_multi_label_format(y: np.ndarray, classes):
      # Some classifiers like catboost might return shape like (n_rows, 1), therefore squeezing the array.
      y = np.squeeze(y) if y.ndim > 1 else y
      if y.ndim == 1:
          kwargs = (
              {"sparse_output": False}
              if version.parse(scikit_version) >= version.parse("1.2")
              else {"sparse": False}
          )
          ohe = OneHotEncoder(
              handle_unknown="ignore", **kwargs
          )  # pylint: disable=unexpected-keyword-arg
          ohe.fit(np.array(classes).reshape(-1, 1))
          return ohe.transform(y.reshape(-1, 1))
      # If after squeeze there are still 2 dimensions, then it must have column for each model class.
      elif y.ndim == 2 and y.shape[1] == len(classes):
          return y
      else:
          raise errors.DeepchecksValueError(
              f"got y with unworkable shape: {y.shape}. {SUPPORTED_MODELS_DOCLINK}"
          )
  
imports: import logging
  import typing
  import warnings
  from numbers import Number
  from typing import TYPE_CHECKING
  import numpy
  import pandas
  from packaging import version
  from sklearn import __version__
  from sklearn.base import ClassifierMixin
  from sklearn.metrics import get_scorer, log_loss, make_scorer, mean_absolute_error, mean_squared_error
  from sklearn.metrics._scorer import _BaseScorer, _ProbaScorer
  from sklearn.preprocessing import OneHotEncoder
  from deepchecks.core import errors
  from deepchecks.tabular.metric_utils.additional_classification_metrics import false_negative_rate_metric, false_positive_rate_metric, roc_auc_per_class, true_negative_rate_metric
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.metrics import get_scorer_name
  from deepchecks.utils.simple_models import PerfectModel
  from deepchecks.utils.typing import BasicModel
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 3 for calling a global function with example usage: _transform_to_multi_label_format(y, classes) and returns: ohe, y