code: def validate_proba(probabilities: np.array, model_classes: t.List):
      """Validate that the number of classes (columns) in probabilities matches the model_classes."""
      if probabilities.shape[1] != len(model_classes):
          raise errors.ModelValidationError(
              f"Model probabilities per class has {probabilities.shape[1]} "
              f"classes while known model classes has {len(model_classes)}. You can set the model's"
              f"classes manually using the model_classes argument in the run function."
          )
  
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 4 for calling a global function with example usage: validate_proba(probabilities, model_classes) without return types