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

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

    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.ExpectedNumInstanceSampler(
                    num_instances=4
                ),
                past_length=train_length,
                future_length=pred_length,
                pick_incomplete=True,
            )
        ]
    )

    assert_serializable(t)

def test_InstanceSplitter(
    start, target, is_train: bool, pick_incomplete: bool
):
    train_length = 100
    pred_length = 13
    t = 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.UniformSplitSampler(p=1.0),
        past_length=train_length,
        future_length=pred_length,
        time_series_fields=["some_time_feature"],
        pick_incomplete=pick_incomplete,
    )

    assert_serializable(t)

    other_feat = np.arange(len(target) + 100)
    data = {
        "start": start,

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,
            )
        ]
    )

    assert_serializable(t)

target_field=FieldName.TARGET,
                output_field="age",
                pred_length=pred_length,
                log_scale=True,
            ),
            transform.AddObservedValuesIndicator(
                target_field=FieldName.TARGET,
                output_field="observed_values",
                convert_nans=False,
            ),
            transform.VstackFeatures(
                output_field="dynamic_feat",
                input_fields=["age", "time_feat"],
                drop_inputs=True,
            ),
            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.ExpectedNumInstanceSampler(
                    num_instances=4
                ),
                past_length=train_length,
                future_length=pred_length,
                time_series_fields=["dynamic_feat", "observed_values"],
                output_NTC=False,
            ),
        ]
    )

    assert_serializable(t)

def create_transformation(self):
            # Model specific input transform
            # Here we use a transformation that randomly
            # selects training samples from all series.
            return 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_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME],
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                TargetDimIndicator(
                    field_name="target_dimension_indicator",
                    target_field=FieldName.TARGET,
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                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.history_length,
                    future_length=self.prediction_length,
                    time_series_fields=[
                        FieldName.FEAT_TIME,
                        FieldName.OBSERVED_VALUES,
                    ],
                    pick_incomplete=self.pick_incomplete,
                ),
                use_marginal_transformation(self.use_marginal_transformation),
            ]

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]
                )

target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                    log_scale=True,
                    dtype=self.dtype,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
                    + (
                        [FieldName.FEAT_DYNAMIC_REAL]
                        if self.use_feat_dynamic_real
                        else []
                    ),
                ),
                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.history_length,
                    future_length=self.prediction_length,
                    time_series_fields=[
                        FieldName.FEAT_TIME,
                        FieldName.OBSERVED_VALUES,
                    ],

pred_length=self.prediction_length,
                ),
                AddAgeFeature(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_AGE,
                    pred_length=self.prediction_length,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE],
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                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=pred_length,
                    output_NTC=False,
                    time_series_fields=[
                        FieldName.FEAT_TIME,
                        FieldName.OBSERVED_VALUES,
                    ],
                ),
                QuantizeScaled(
                    bin_edges=bin_edges.tolist(),
                    future_target="future_target",

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(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                transform.AsNumpyArray(
                    field=FieldName.FEAT_STATIC_CAT, expected_ndim=1
                ),
                transform.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],
                ),