src/gluonts/nursery/SCott/pts/model/tempflow/tempflow_estimator.py [74:162]:
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        self.num_parallel_samples = num_parallel_samples
        self.dropout_rate = dropout_rate
        self.cardinality = cardinality
        self.embedding_dimension = embedding_dimension

        self.flow_type = flow_type
        self.n_blocks = n_blocks
        self.hidden_size = hidden_size
        self.n_hidden = n_hidden
        self.conditioning_length = conditioning_length
        self.dequantize = dequantize

        self.lags_seq = (
            lags_seq
            if lags_seq is not None
            else get_fourier_lags_for_frequency(freq_str=freq)
        )

        self.time_features = (
            time_features
            if time_features is not None
            else fourier_time_features_from_frequency_str(self.freq)
        )

        self.history_length = self.context_length + max(self.lags_seq)
        self.pick_incomplete = pick_incomplete
        self.scaling = scaling

    def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(
                    field=FieldName.TARGET,
                    expected_ndim=2,
                ),
                # maps the target to (1, T)
                # if the target data is uni dimensional
                ExpandDimArray(
                    field=FieldName.TARGET,
                    axis=None,
                ),
                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=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]
                ),
                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,
                ),
                RenameFields(
                    {
                        f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
                        f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
                    }
                ),
            ]
        )

    def create_training_network(
        self, device: torch.device
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src/gluonts/nursery/SCott/pts/model/transformer_tempflow/transformer_tempflow_estimator.py [82:170]:
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        self.num_parallel_samples = num_parallel_samples
        self.dropout_rate = dropout_rate
        self.cardinality = cardinality
        self.embedding_dimension = embedding_dimension

        self.flow_type = flow_type
        self.n_blocks = n_blocks
        self.hidden_size = hidden_size
        self.n_hidden = n_hidden
        self.conditioning_length = conditioning_length
        self.dequantize = dequantize

        self.lags_seq = (
            lags_seq
            if lags_seq is not None
            else get_fourier_lags_for_frequency(freq_str=freq)
        )

        self.time_features = (
            time_features
            if time_features is not None
            else fourier_time_features_from_frequency_str(self.freq)
        )

        self.history_length = self.context_length + max(self.lags_seq)
        self.pick_incomplete = pick_incomplete
        self.scaling = scaling

    def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(
                    field=FieldName.TARGET,
                    expected_ndim=2,
                ),
                # maps the target to (1, T)
                # if the target data is uni dimensional
                ExpandDimArray(
                    field=FieldName.TARGET,
                    axis=None,
                ),
                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=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]
                ),
                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,
                ),
                RenameFields(
                    {
                        f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
                        f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
                    }
                ),
            ]
        )

    def create_training_network(
        self, device: torch.device
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