__________________________________ test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_data_not_an_array] __________________________________

estimator = ColumnTransformer(transformers=[('trans1', StandardScaler(), [0, 1])])
check = functools.partial(<function check_transformer_data_not_an_array at 0x123672de0>, 'ColumnTransformer')
request = <FixtureRequest for <Function test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_data_not_an_array]>>

    @parametrize_with_checks(list(_tested_estimators()))
    def test_estimators(estimator, check, request):
        # Common tests for estimator instances
        with ignore_warnings(
            category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
        ):
>           check(estimator)

sklearn/tests/test_common.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
sklearn/utils/_testing.py:140: in wrapper
    return fn(*args, **kwargs)
sklearn/utils/estimator_checks.py:1594: in check_transformer_data_not_an_array
    _check_transformer(name, transformer, this_X, this_y)
sklearn/utils/estimator_checks.py:1646: in _check_transformer
    transformer.fit(X, y_)
sklearn/compose/_column_transformer.py:951: in fit
    self.fit_transform(X, y=y, **params)
sklearn/utils/_set_output.py:319: in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
sklearn/base.py:1244: in wrapper
    return fit_method(estimator, *args, **kwargs)
sklearn/compose/_column_transformer.py:997: in fit_transform
    self._validate_column_callables(X)
sklearn/compose/_column_transformer.py:551: in _validate_column_callables
    transformer_to_input_indices[name] = _get_column_indices(X, columns)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X = <sklearn.utils.estimator_checks._NotAnArray object at 0x123411e20>, key = [0, 1]

    def _get_column_indices(X, key):
        """Get feature column indices for input data X and key.
    
        For accepted values of `key`, see the docstring of
        :func:`_safe_indexing`.
        """
        key_dtype = _determine_key_type(key)
        if _use_interchange_protocol(X):
            return _get_column_indices_interchange(X.__dataframe__(), key, key_dtype)
    
>       n_columns = X.shape[1]
E       AttributeError: '_NotAnArray' object has no attribute 'shape'

sklearn/utils/_indexing.py:333: AttributeError
----------------------------------------------------------------------------------- Captured stdout setup ------------------------------------------------------------------------------------
I: Seeding RNGs with 1235426376
================================================================================== short test summary info ===================================================================================
FAILED sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_data_not_an_array] - AttributeError: '_NotAnArray' object has no attribute 'shape'