code: def _predict_with_local_model(
      self,
      time_series: pd.Series,
      local_model_args: dict,
  ) -> pd.DataFrame:
      # intermittent demand models clip their predictions at 0 or lower if the time series has lower values
      predictions = super()._predict_with_local_model(
          time_series=time_series, local_model_args=local_model_args
      )
      return predictions.clip(lower=min(0, time_series.min()))
  
imports: import logging
  from typing import Any, Dict, Type
  import numpy
  import pandas
  from .abstract_local_model import AbstractLocalModel
label_desc: Use image node_id 2 for calling the AbstractStatsForecastIntermittentDemandModel obj's underlying member method code with example usage: obj._predict_with_local_model(time_series, local_model_args) and returns: predictions