docstr: Remove series that are too short for chosen differencing from data and generate naive forecast for them.
  
  Returns
  -------
  data_long : TimeSeriesDataFrame
      Data containing only time series that are long enough for the model to predict.
  known_covariates_long : TimeSeriesDataFrame or None
      Future known covariates containing only time series that are long enough for the model to predict.
  forecast_for_short_series : TimeSeriesDataFrame or None
      Seasonal naive forecast for short series, if there are any in the dataset.
code: def _remove_short_ts_and_generate_fallback_forecast(
      self,
      data: TimeSeriesDataFrame,
      known_covariates: Optional[TimeSeriesDataFrame] = None,
  ) -> Tuple[
      TimeSeriesDataFrame,
      Optional[TimeSeriesDataFrame],
      Optional[TimeSeriesDataFrame],
  ]:
      """Remove series that are too short for chosen differencing from data and generate naive forecast for them.
  
      Returns
      -------
      data_long : TimeSeriesDataFrame
          Data containing only time series that are long enough for the model to predict.
      known_covariates_long : TimeSeriesDataFrame or None
          Future known covariates containing only time series that are long enough for the model to predict.
      forecast_for_short_series : TimeSeriesDataFrame or None
          Seasonal naive forecast for short series, if there are any in the dataset.
      """
      ts_lengths = data.num_timesteps_per_item()
      short_series = ts_lengths.index[
          ts_lengths <= self._sum_of_differences
      ]
      if len(short_series) > 0:
          logger.warning(
              f"Warning: {len(short_series)} time series ({len(short_series) / len(ts_lengths):.1%}) are shorter "
              f"than {self._sum_of_differences} and cannot be predicted by {self.name}. "
              "Fallback model SeasonalNaive is used for these time series."
          )
          data_short = data.query("item_id in @short_series")
          seasonal_naive = SeasonalNaiveModel(
              freq=self.freq, prediction_length=self.prediction_length
          )
          seasonal_naive.fit(train_data=data_short)
          forecast_for_short_series = seasonal_naive.predict(data_short)
  
          data_long = data.query("item_id not in @short_series")
          if known_covariates is not None:
              known_covariates_long = known_covariates.query(
                  "item_id not in @short_series"
              )
          else:
              known_covariates_long = None
      else:
          data_long = data
          known_covariates_long = known_covariates
          forecast_for_short_series = None
      return data_long, known_covariates_long, forecast_for_short_series
  
imports: import logging
  import math
  import os
  import time
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.tabular import TabularPredictor
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID, TIMESTAMP, TimeSeriesDataFrame
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.models.local import SeasonalNaiveModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from autogluon.timeseries.utils.warning_filters import warning_filter
label_desc: Use image node_id 12 for calling the AbstractMLForecastModel obj's underlying member method code with example usage: obj._remove_short_ts_and_generate_fallback_forecast(data, known_covariates) and returns: data_long,  known_covariates_long,  forecast_for_short_series