code: def __init__(
      self,
      start_p=2,
      d=None,
      start_q=2,
      max_p=5,
      max_d=2,
      max_q=5,
      start_P=1,
      D=None,
      start_Q=1,
      max_P=2,
      max_D=1,
      max_Q=2,
      max_order=5,
      m=1,
      seasonal=True,
      stationary=False,
      information_criterion="aic",
      alpha=0.05,
      test="kpss",
      seasonal_test="ocsb",
      stepwise=True,
      n_jobs=1,
      start_params=None,
      trend=None,
      method="lbfgs",
      maxiter=50,
      offset_test_args=None,
      seasonal_test_args=None,
      suppress_warnings=True,
      error_action="trace",
      trace=False,
      random=False,
      random_state=None,
      n_fits=10,
      out_of_sample_size=0,
      scoring="mse",
      scoring_args=None,
      with_intercept="auto",
      **kwargs,
  ):
      self.start_p = start_p
      self.d = d
      self.start_q = start_q
      self.max_p = max_p
      self.max_d = max_d
      self.max_q = max_q
      self.start_P = start_P
      self.D = D
      self.start_Q = start_Q
      self.max_P = max_P
      self.max_D = max_D
      self.max_Q = max_Q
      self.max_order = max_order
      self.m = m
      self.seasonal = seasonal
      self.stationary = stationary
      self.information_criterion = information_criterion
      self.alpha = alpha
      self.test = test
      self.seasonal_test = seasonal_test
      self.stepwise = stepwise
      self.n_jobs = n_jobs
      self.start_params = start_params
      self.trend = trend
      self.method = method
      self.maxiter = maxiter
      self.offset_test_args = offset_test_args
      self.seasonal_test_args = seasonal_test_args
      self.suppress_warnings = suppress_warnings
      self.error_action = error_action
      self.trace = trace
      self.random = random
      self.random_state = random_state
      self.n_fits = n_fits
      self.out_of_sample_size = out_of_sample_size
      self.scoring = scoring
      self.scoring_args = scoring_args
      self.with_intercept = with_intercept
  
      kwargs = _warn_for_deprecations(**kwargs)
      self.kwargs = kwargs
  
imports: import numpy
  from sklearn.linear_model import LinearRegression
  import functools
  import time
  import warnings
  from ..base import BaseARIMA
  from .None import _doc
  from .None import _validation
  from .utils import ndiffs, is_constant, nsdiffs
  from ..utils import diff, is_iterable, check_endog
  from ..utils.metaestimators import if_has_delegate
  from ._context import AbstractContext, ContextType
  from .None import _auto_solvers
  from ..compat.numpy import DTYPE
  from ..compat import statsmodels
label_desc: Use image node_id 1 to create a new AutoARIMA object from inherited base classes: BaseARIMA with example: obj = AutoARIMA(start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, m, seasonal, stationary, information_criterion, alpha, test, seasonal_test, stepwise, n_jobs, start_params, trend, method, maxiter, offset_test_args, seasonal_test_args, suppress_warnings, error_action, trace, random, random_state, n_fits, out_of_sample_size, scoring, scoring_args, with_intercept)