class_docstr: A naive forecaster that always returns 0 forecasts across the prediction horizon, where the prediction
  intervals are computed using conformal prediction.
  
  Other Parameters
  ----------------
  n_jobs : int or float, default = 0.5
      Number of CPU cores used to fit the models in parallel.
      When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used.
      When set to a positive integer, that many cores are used.
      When set to -1, all CPU cores are used.
  max_ts_length : int, default = 2500
      If not None, only the last ``max_ts_length`` time steps of each time series will be used to train the model.
      This significantly speeds up fitting and usually leads to no change in accuracy.
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code: def _get_point_forecast(
      self,
      time_series: pd.Series,
      local_model_args: Dict,
  ):
      return np.zeros(self.prediction_length)
  
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 ZeroModel obj's underlying member method code with example usage: obj._get_point_forecast(time_series, local_model_args) and returns: np