How to use the gluonts.dataset.field_names.FieldName.FORECAST_START function in gluonts

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,
            )

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)

    scale_hist = ScaleHistogram()

    repetition = 200
    for i in range(repetition):

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),
            ]

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",
                    past_target="past_target",
                ),

log_scale=True,
                ),
                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,
                    ],

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,
                    ],

def __init__(
        self,
        past_interval_length: float,
        future_interval_length: float,
        train_sampler: ContinuousTimePointSampler,
        target_field: str = FieldName.TARGET,
        start_field: str = FieldName.START,
        end_field: str = "end",
        forecast_start_field: str = FieldName.FORECAST_START,
    ) -> None:

        assert (
            future_interval_length > 0
        ), "Prediction interval must have length greater than 0."

        self.train_sampler = train_sampler
        self.past_interval_length = past_interval_length
        self.future_interval_length = future_interval_length
        self.target_field = target_field
        self.start_field = start_field
        self.end_field = end_field
        self.forecast_start_field = forecast_start_field

AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0.0]
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
                CanonicalInstanceSplitter(
                    target_field=FieldName.TARGET,
                    is_pad_field=FieldName.IS_PAD,
                    start_field=FieldName.START,
                    forecast_start_field=FieldName.FORECAST_START,
                    instance_sampler=TestSplitSampler(),
                    time_series_fields=[FieldName.FEAT_TIME],
                    instance_length=self.context_length,
                    use_prediction_features=True,
                    prediction_length=self.prediction_length,
                ),

def __init__(
        self,
        past_interval_length: float,
        future_interval_length: float,
        train_sampler: ContinuousTimePointSampler,
        target_field: str = FieldName.TARGET,
        start_field: str = FieldName.START,
        end_field: str = "end",
        forecast_start_field: str = FieldName.FORECAST_START,
    ) -> None:

        assert (
            future_interval_length > 0
        ), "Prediction interval must have length greater than 0."

        self.train_sampler = train_sampler
        self.past_interval_length = past_interval_length
        self.future_interval_length = future_interval_length
        self.target_field = target_field
        self.start_field = start_field
        self.end_field = end_field
        self.forecast_start_field = forecast_start_field

),
                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],
                ),