class_docstr: Config object for default forecasting model.
docstr: :param max_forecast_steps: Max # of steps we would like to forecast for.
  :param target_seq_index: The index of the univariate (amongst all univariates in a general multivariate time
      series) whose value we would like to forecast.
  :param granularity: the granularity at which the input time series should be sampled, e.g. "5min", "1d", etc.
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code: def __init__(
      self,
      model=None,
      max_forecast_steps: int = None,
      target_seq_index: int = None,
      granularity: str = None,
      **kwargs,
  ):
      """
      :param max_forecast_steps: Max # of steps we would like to forecast for.
      :param target_seq_index: The index of the univariate (amongst all univariates in a general multivariate time
          series) whose value we would like to forecast.
      :param granularity: the granularity at which the input time series should be sampled, e.g. "5min", "1d", etc.
      """
      self.granularity = granularity
      super().__init__(
          model=model,
          max_forecast_steps=max_forecast_steps,
          target_seq_index=target_seq_index,
          **kwargs,
      )
      assert self.base_model is None or isinstance(
          self.base_model, ForecasterBase
      )
  
imports: import logging
  from typing import Optional, Tuple
  from merlion.models.factory import ModelFactory
  from merlion.models.layers import LayeredDetector, LayeredForecaster, LayeredModelConfig
  from merlion.models.anomaly.base import DetectorBase
  from merlion.models.forecast.base import ForecasterBase
  from merlion.utils import TimeSeries
label_desc: Use image node_id 1 to create a new DefaultForecasterConfig object from inherited base classes: LayeredModelConfig with example: obj = DefaultForecasterConfig(model, max_forecast_steps, target_seq_index, granularity)