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

grouped_data = self._transform_target(self._left_pad_data, dataset)
        # splits test dataset with rolling date into N R^d time series where
        # N is the number of rolling evaluation dates
        split_dataset = np.split(
            grouped_data[FieldName.TARGET], self.num_test_dates
        )

        all_entries = list()
        for dataset_at_test_date in split_dataset:
            grouped_data = dict()
            grouped_data[FieldName.TARGET] = np.array(
                list(dataset_at_test_date), dtype=np.float32
            )
            grouped_data = self._restrict_max_dimensionality(grouped_data)
            grouped_data[FieldName.START] = self.first_timestamp
            grouped_data[FieldName.FEAT_STATIC_CAT] = [0]
            all_entries.append(grouped_data)

        return ListDataset(
            all_entries, freq=self.frequency, one_dim_target=False
        )

def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                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,
                ),

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(
                    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],
                ),

time_features=time_features_from_frequency_str(self.freq),
                    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(),

def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                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,
                ),

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,
                    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,
                    ],
                ),

#  This function generates the test and train json files which will be converted to csv format
    if not os.path.exists(os.path.dirname(filename)):
        os.makedirs(os.path.dirname(filename))
    with open(filename, "w") as json_file:
        for ts in time_series:
            if is_missing:
                target = []  # type: List
                # For Forecast don't output feat_static_cat and feat_static_real
                for j, val in enumerate(ts[FieldName.TARGET]):
                    # only add ones that are not missing
                    if j != 0 and j % num_missing == 0:
                        target.append(None)
                    else:
                        target.append(val)
                ts[FieldName.TARGET] = target
            ts.pop(FieldName.FEAT_STATIC_CAT, None)
            ts.pop(FieldName.FEAT_STATIC_REAL, None)
            # Chop features in training set
            if (
                FieldName.FEAT_DYNAMIC_REAL in ts.keys()
                and "train" in filename
            ):
                # TODO: Fix for missing values
                for i, feat_dynamic_real in enumerate(
                    ts[FieldName.FEAT_DYNAMIC_REAL]
                ):
                    ts[FieldName.FEAT_DYNAMIC_REAL][i] = feat_dynamic_real[
                        : len(ts[FieldName.TARGET])
                    ]
            json.dump(ts, json_file)
            json_file.write("\n")

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(
                    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],
                ),

def create_transformation(self) -> Transformation:
        return Chain(
            trans=[
                AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
                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,
                ),
                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=TestSplitSampler(),
                    time_series_fields=[FieldName.FEAT_TIME],
                    past_length=self.context_length,
                    future_length=self.prediction_length,
                ),

CategoricalFeatureInfo(
                name="feat_static_cat_000", cardinality="10"
            )
        ],
        feat_static_real=[BasicFeatureInfo(name="feat_static_real_000")],
    )

    start_date = "2000-01-01 00:00:00"

    train_ds = ListDataset(
        data_iter=[
            {
                FieldName.ITEM_ID: str(i),
                FieldName.START: start_date,
                FieldName.TARGET: [float(i)] * 24,
                FieldName.FEAT_STATIC_CAT: [i],
                FieldName.FEAT_STATIC_REAL: [float(i)],
            }
            for i in range(10)
        ],
        freq=metadata.freq,
    )

    test_ds = ListDataset(
        data_iter=[
            {
                FieldName.ITEM_ID: str(i),
                FieldName.START: start_date,
                FieldName.TARGET: [float(i)] * 30,
                FieldName.FEAT_STATIC_CAT: [i],
                FieldName.FEAT_STATIC_REAL: [float(i)],
            }