orbit/template/dlt.py [578:600]:
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        full_len = prediction_meta[PredictionMetaKeys.END_INDEX.value]

        ################################################################
        # Model Attributes
        ################################################################
        model = deepcopy(posterior_estimates)
        for k, v in model.items():
            model[k] = torch.from_numpy(v)

        # We can pull any arbitrary value from teh dictionary because we hold the
        # safe assumption: the length of the first dimension is always the number of samples
        # thus can be safely used to determine `num_sample`. If predict_method is anything
        # other than full, the value here should be 1
        arbitrary_posterior_value = list(model.values())[0]
        num_sample = arbitrary_posterior_value.shape[0]

        # seasonality components
        seasonality_levels = model.get(
            SeasonalitySamplingParameters.SEASONALITY_LEVELS.value
        )
        seasonality_smoothing_factor = model.get(
            SeasonalitySamplingParameters.SEASONALITY_SMOOTHING_FACTOR.value
        )
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orbit/template/ets.py [186:208]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        full_len = prediction_meta[PredictionMetaKeys.END_INDEX.value]

        ################################################################
        # Model Attributes
        ################################################################
        model = deepcopy(posterior_estimates)
        for k, v in model.items():
            model[k] = torch.from_numpy(v)

        # We can pull any arbitrary value from teh dictionary because we hold the
        # safe assumption: the length of the first dimension is always the number of samples
        # thus can be safely used to determine `num_sample`. If predict_method is anything
        # other than full, the value here should be 1
        arbitrary_posterior_value = list(model.values())[0]
        num_sample = arbitrary_posterior_value.shape[0]

        # seasonality components
        seasonality_levels = model.get(
            SeasonalitySamplingParameters.SEASONALITY_LEVELS.value
        )
        seasonality_smoothing_factor = model.get(
            SeasonalitySamplingParameters.SEASONALITY_SMOOTHING_FACTOR.value
        )
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