class_docstr: Intermittent demand forecasting model using Croston's model with the Syntetos-Boylan
  bias correction approach [SyntetosBoylan2001]_.
  
  Based on `statsforecast.models.CrostonSBA <https://nixtla.mintlify.app/statsforecast/docs/models/crostonsba.html>`_.
  
  
  References
  ----------
  .. [SyntetosBoylan2001] Syntetos, Aris A., and John E. Boylan. "On the bias of intermittent
      demand estimates." International journal of production economics 71.1-3 (2001): 457-466.
  
  
  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_model_type(self):
      from statsforecast.models import CrostonSBA
  
      return CrostonSBA
  
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 1 for calling the CrostonSBAModel obj's underlying member method code with example usage: obj._get_model_type() and returns: CrostonSBA