class_docstr: Base class for estimators using the incremental `fit`.
  
  Subclasses must implement the following abstract method
  
  * _additional_calls
class_docstr_tok: ["Base","class","for","estimators","using","the","incremental","`","fit","`",".","Subclasses","must","implement","the","following","abstract","method","*","_additional_calls"]
code: def _check_is_fitted(self, method_name):
      return check_is_fitted(self, "best_estimator_")
  
imports: from __future__ import division
  import itertools
  import logging
  import operator
  import sys
  from collections import defaultdict, namedtuple
  from copy import deepcopy
  from time import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from warnings import warn
  import dask
  import dask.array
  import dask.dataframe
  import numpy
  import scipy.stats
  import toolz
  from dask.distributed import Future, default_client, futures_of, wait
  from distributed.utils import log_errors
  from sklearn.base import BaseEstimator, clone
  from sklearn.linear_model import SGDClassifier
  from sklearn.metrics import check_scoring
  from sklearn.model_selection import ParameterGrid, ParameterSampler
  from sklearn.utils import check_random_state
  from sklearn.utils.metaestimators import available_if
  from sklearn.utils.validation import check_is_fitted
  from .._compat import DISTRIBUTED_2021_02_0, annotate, dummy_context
  from .._typing import ArrayLike, Int
  from .._utils import LoggingContext
  from ..wrappers import ParallelPostFit
  from ._split import train_test_split
  from .utils import estimator_has
label_desc: Use image node_id 9 for calling the BaseIncrementalSearchCV obj's underlying member method code with example usage: obj._check_is_fitted(method_name) and returns: check_is_fitted