def METHOD_NAME(internal_type, data):
    """Test conversion function.
    The conversion functionality of convertCollection is tested in the utils module.
    This test runs a subset of these but also checks classifiers with multiple
    internal types.
    """
    cls = BaseCollectionEstimator()
    # Equal length should default to numpy3D
    X = EQUAL_LENGTH_UNIVARIATE[data]
    cls.metadata_ = cls._check_X(X)
    X2 = cls._convert_X(X)
    assert get_type(X2) == cls.get_tag("X_inner_mtype")
    # Add the internal_type tag to cls, should still all revert to numpy3D
    cls.set_tags(**{"X_inner_mtype": ["numpy3D", internal_type]})
    X2 = cls._convert_X(X)
    assert get_type(X2) == "numpy3D"
    # Set cls inner type to just internal_type, should convert to internal_type
    cls.set_tags(**{"X_inner_mtype": internal_type})
    X2 = cls._convert_X(X)
    assert get_type(X2) == internal_type
    # Set to single type but in a list
    cls.set_tags(**{"X_inner_mtype": [internal_type]})
    X2 = cls._convert_X(X)
    assert get_type(X2) == internal_type
    # Set to the lowest priority data type, should convert to internal_type
    cls.set_tags(**{"X_inner_mtype": ["nested_univ", internal_type]})
    X2 = cls._convert_X(X)
    assert get_type(X2) == internal_type
    if data in UNEQUAL_LENGTH_UNIVARIATE.keys():
        if internal_type in UNEQUAL_LENGTH_UNIVARIATE.keys():
            cls.set_tags(**{"capability:unequal_length": True})
            cls.set_tags(**{"X_inner_mtype": ["nested_univ", "np-list", internal_type]})
            X = UNEQUAL_LENGTH_UNIVARIATE[data]
            X2 = cls._convert_X(X)
            assert get_type(X2) == "np-list"