def test_input_transformed_multiple_series(self):
        """
        Tests for correct transformation of multiple series when
        different param values are used for different parallel
        jobs (i.e. test that `parallel_params` argument is treated
        correctly). Also tests that transformer correctly handles
        being provided with fewer input series than fixed parameter
        value sets.
        """
        test_input_1 = constant_timeseries(value=1, length=10)
        test_input_2 = constant_timeseries(value=2, length=11)

        # Don't have different params for different jobs:
        mock = self.DataTransformerMock(scale=2, translation=10, parallel_params=False)
        (transformed_1, transformed_2) = mock.transform((test_input_1, test_input_2))
        # 2 * 1 + 10 = 12
        assert transformed_1 == constant_timeseries(value=12, length=10)
        # 2 * 2 + 10 = 14
        assert transformed_2 == constant_timeseries(value=14, length=11)

        # Have different `scale` param for different jobs:
        mock = self.DataTransformerMock(
            scale=(2, 3), translation=10, parallel_params=["_scale"]
        )
        (transformed_1, transformed_2) = mock.transform((test_input_1, test_input_2))
        # 2 * 1 + 10 = 12
        assert transformed_1 == constant_timeseries(value=12, length=10)
        # 3 * 2 + 10 = 16
        assert transformed_2 == constant_timeseries(value=16, length=11)

        # If only one timeseries provided, should apply parameters defined for
        # for the first to that series:
        transformed_1 = mock.transform(test_input_1)
        assert transformed_1 == constant_timeseries(value=12, length=10)

        # Have different `scale`, `translation`, and `stack_samples` params for different jobs:
        mock = self.DataTransformerMock(
            scale=(2, 3),
            translation=(10, 11),
            stack_samples=(False, True),
            mask_components=(False, False),
            parallel_params=True,
        )
        (transformed_1, transformed_2) = mock.transform((test_input_1, test_input_2))
        # 2 * 1 + 10 = 12
        assert transformed_1 == constant_timeseries(value=12, length=10)
        # 3 * 2 + 11 = 17
        assert transformed_2 == constant_timeseries(value=17, length=11)

        # If only one timeseries provided, should apply parameters defined for
        # for the first to that series:
        transformed_1 = mock.transform(test_input_1)
        assert transformed_1 == constant_timeseries(value=12, length=10)

        # Specify three sets of fixed params, but pass only one or two series as inputs
        # to `transform`; transformer should apply `i`th set of fixed params to the `i`th
        # input passed to `transform`
        mock = self.DataTransformerMock(
            scale=(2, 3, 4),
            translation=(10, 11, 12),
            stack_samples=(False, True, False),
            mask_components=(False, False, False),
            parallel_params=True,
        )
        # If single series provided to transformer with three sets of
        # fixed params, should transform using the first set of fixed
        # parameters:
        transformed_1 = mock.transform(test_input_1)
        assert transformed_1 == constant_timeseries(value=12, length=10)
        # If two series provided to transformer with three sets of
        # fixed params, should transform using the first and second set of fixed
        # parameters:
        transformed_1, transformed_2 = mock.transform((test_input_1, test_input_2))
        assert transformed_1 == constant_timeseries(value=12, length=10)
        assert transformed_2 == constant_timeseries(value=17, length=11)

        Tests for correct transformation of multiple series when
        different param values are used for different parallel
        jobs (i.e. test that `parallel_params` argument is treated
        correctly). Also tests that transformer correctly handles
        being provided with fewer input series than fixed parameter
        value sets.
        
darts/tests/dataprocessing/transformers/test_base_data_transformer.py