class_docstr: Dummy classifier that accepts parameters a, b, ... z.
  
  These parameter don't affect the predictions and are useful for fast
  grid searching.
code: def __init__(
      self,
      strategy="stratified",
      random_state=None,
      constant=None,
      **kwargs,
  ):
      super().__init__(
          strategy=strategy,
          random_state=random_state,
          constant=constant,
      )
  
imports: from math import ceil
  import numpy
  import pytest
  from scipy.stats import expon, norm, randint
  from sklearn.datasets import make_classification
  from sklearn.dummy import DummyClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import GroupKFold, GroupShuffleSplit, HalvingGridSearchCV, HalvingRandomSearchCV, KFold, LeaveOneGroupOut, LeavePGroupsOut, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit
  from sklearn.model_selection._search_successive_halving import _SubsampleMetaSplitter, _top_k
  from sklearn.model_selection.tests.test_search import check_cv_results_array_types, check_cv_results_keys
  from sklearn.svm import SVC, LinearSVC
label_desc: Use image node_id 1 to create a new FastClassifier object from inherited base classes: DummyClassifier with example: obj = FastClassifier(strategy, random_state, constant)