code: def predict_in_sample(
      self,
      X=None,
      start=None,
      end=None,
      dynamic=False,
      return_conf_int=False,
      alpha=0.05,
      typ="levels",
  ):
      return self.model_.predict_in_sample(
          X=X,
          start=start,
          end=end,
          dynamic=dynamic,
          return_conf_int=return_conf_int,
          alpha=alpha,
          typ=typ,
      )
  
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 3 for calling the AutoARIMA obj's underlying member method code with example usage: obj.predict_in_sample(X, start, end, dynamic, return_conf_int, alpha, typ) and returns: self