class_docstr: Wrapper for ``pmdarima.arima.AutoARIMA``.
  It currently does not handle the regressor issue when there is
  gap between train and predict periods.
  
  Parameters
  ----------
  score_func : callable
      see ``BaseForecastEstimator``.
  coverage : float between [0.0, 1.0]
      see ``BaseForecastEstimator``.
  null_model_params : dict with arguments to define DummyRegressor null model, optional, default=None
      see ``BaseForecastEstimator``.
  regressor_cols: `list` [`str`], optional, default None
      A list of regressor columns used during training and prediction.
      If None, no regressor columns are used.
  
  See ``AutoArima`` documentation for rest of the parameter descriptions:
  
          * https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA
  
  Attributes
  ----------
  model : ``AutoArima`` object
      Auto arima model object
  fit_df : `pandas.DataFrame` or None
      The training data used to fit the model.
  forecast : `pandas.DataFrame`
      Output of the predict method of ``AutoArima``.
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code: def summary(self):
      BaseForecastEstimator.summary(self)
      # AutoArima summary
      return self.model.summary()
  
imports: from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Union
  import numpy
  import pandas
  from pmdarima.arima import AutoARIMA
  from sklearn.metrics import mean_squared_error
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.time_properties import infer_freq
  from greykite.sklearn.estimator.base_forecast_estimator import BaseForecastEstimator
label_desc: Use image node_id 4 for calling the AutoArimaEstimator obj's underlying member method code with example usage: obj.summary() and returns: self