def METHOD_NAME(self, context: Context) -> CheckResult:
    """Run check.
    Returns
    -------
    CheckResult
        value: dictionary containing the domain classifier auc and a dict of column name to its feature
        importance as calculated for the domain classifier model.
        display: distribution graph for each column for the columns most explaining the dataset difference,
        comparing the train and test distributions.
    Raises
    ------
    DeepchecksValueError
        If the object is not a Dataset or DataFrame instance
    """
    train_dataset = context.train
    test_dataset = context.test
    sample_size = min(self.sample_size, train_dataset.n_samples, test_dataset.n_samples)
    values_dict, displays = run_multivariable_drift_for_embeddings(
        train_dataset=train_dataset,
        test_dataset=test_dataset,
        sample_size=sample_size, random_state=self.random_state,
        test_size=self.test_size,
        num_samples_in_display=self.num_samples_in_display,
        dimension_reduction_method=self.dimension_reduction_method,
        with_display=context.with_display,
        model_classes=context.model_classes
    )
    return CheckResult(value=values_dict, display=displays, header='Embeddings Drift')