How to use the gluonts.transform.Chain function in gluonts

# generates 2 ** N - 1 timeseries with constant increasing values
        n = 2 ** N - 1

        targets = np.arange(n * train_length).reshape((n, train_length))

        return ListDataset(
            [
                {"start": "2012-01-01", "target": targets[i, :]}
                for i in range(n)
            ],
            freq="D",
        )

    ds = make_dataset(1, 20)

    trans = transform.Chain(
        trans=[
            transform.AddAgeFeature(
                target_field=FieldName.TARGET,
                output_field="age",
                pred_length=10,
            ),
            ForkingSequenceSplitter(
                train_sampler=TSplitSampler(),
                time_series_fields=["age"],
                enc_len=5,
                dec_len=3,
            ),
        ]
    )

    out = trans(iter(ds), is_train=True)

def test_BucketInstanceSampler():
    N = 6
    train_length = 2
    pred_length = 1
    ds = make_dataset(N, train_length)

    dataset_stats = calculate_dataset_statistics(ds)

    t = transform.Chain(
        trans=[
            transform.InstanceSplitter(
                target_field=FieldName.TARGET,
                is_pad_field=FieldName.IS_PAD,
                start_field=FieldName.START,
                forecast_start_field=FieldName.FORECAST_START,
                train_sampler=transform.BucketInstanceSampler(
                    dataset_stats.scale_histogram
                ),
                past_length=train_length,
                future_length=pred_length,
                pick_incomplete=True,
            )
        ]
    )

def test_Transformation():
    train_length = 100
    ds = gluonts.dataset.common.ListDataset(
        [{"start": "2012-01-01", "target": [0.2] * train_length}], freq="1D"
    )

    pred_length = 10

    t = transform.Chain(
        trans=[
            transform.AddTimeFeatures(
                start_field=FieldName.START,
                target_field=FieldName.TARGET,
                output_field="time_feat",
                time_features=[
                    time_feature.DayOfWeek(),
                    time_feature.DayOfMonth(),
                    time_feature.MonthOfYear(),
                ],
                pred_length=pred_length,
            ),
            transform.AddAgeFeature(
                target_field=FieldName.TARGET,
                output_field="age",
                pred_length=pred_length,

def create_transformation(
        self, bin_edges: np.ndarray, pred_length: int
    ) -> transform.Transformation:
        return Chain(
            [
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,

def create_transformation(self) -> Transformation:
        remove_field_names = [FieldName.FEAT_DYNAMIC_CAT]
        if not self.use_feat_static_real:
            remove_field_names.append(FieldName.FEAT_STATIC_REAL)
        if not self.use_feat_dynamic_real:
            remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)

        return Chain(
            [RemoveFields(field_names=remove_field_names)]
            + (
                [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
                if not self.use_feat_static_cat
                else []
            )
            + (
                [
                    SetField(
                        output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
                    )
                ]
                if not self.use_feat_static_real
                else []
            )
            + [

def create_transformation(self) -> Transformation:
        return Chain(
            [
                InstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    train_sampler=ExpectedNumInstanceSampler(num_instances=1),
                    past_length=self.context_length,
                    future_length=self.prediction_length,
                    time_series_fields=[],  # [FieldName.FEAT_DYNAMIC_REAL]
                )

def create_transformation(self) -> Transformation:
        remove_field_names = [
            FieldName.FEAT_DYNAMIC_CAT,
            FieldName.FEAT_STATIC_REAL,
        ]
        if not self.use_feat_dynamic_real:
            remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)

        return Chain(
            [RemoveFields(field_names=remove_field_names)]
            + (
                [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
                if not self.use_feat_static_cat
                else []
            )
            + [
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                # gives target the (1, T) layout
                ExpandDimArray(field=FieldName.TARGET, axis=0),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                # Unnormalized seasonal features

def create_transformation(self) -> transform.Transformation:
        return transform.Chain(
            trans=[
                transform.AsNumpyArray(
                    field=FieldName.TARGET, expected_ndim=1
                ),
                transform.AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=time_features_from_frequency_str(self.freq),
                    pred_length=self.prediction_length,
                ),
                transform.VstackFeatures(
                    output_field=FieldName.FEAT_DYNAMIC_REAL,
                    input_fields=[FieldName.FEAT_TIME],
                ),
                transform.SetFieldIfNotPresent(