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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit(self, X, y=None, groups=None, **fit_params):
 """Run fit with all sets of parameters. Parameters X : array-like, shape = [n_samples, n_features] Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. groups : array-like, shape = [n_samples], optional Group labels for the samples used while splitting the dataset into train/test set. **fit_params Parameters passed to the ``fit`` method of the estimator """

        estimator = self.estimator
        from sklearn.metrics.scorer import _check_multimetric_scoring

        scorer, multimetric = _check_multimetric_scoring(
            estimator, scoring=self.scoring
        )
        if not multimetric:
            scorer = scorer["score"]
        self.multimetric_ = multimetric

        if self.multimetric_:
            if self.refit is not False and (
                not isinstance(self.refit, str)
                # This will work for both dict / list (tuple)
                or self.refit not in scorer
            ):
                raise ValueError(
                    "For multi-metric scoring, the parameter "
                    "refit must be set to a scorer key "
                    "to refit an estimator with the best "
                    "parameter setting on the whole data and "
                    "make the best_* attributes "
                    "available for that metric. If this is "
                    "not needed, refit should be set to "
                    "False explicitly. %r was ."
                    "passed." % self.refit
                )
        self.scorer_ = scorer

        error_score = self.error_score
        if not (isinstance(error_score, numbers.Number) or error_score == "raise"):
            raise ValueError(
                "error_score must be the string 'raise' or a" " numeric value."
            )

        dsk, keys, n_splits = build_graph(
            estimator,
            self.cv,
            self.scorer_,
            list(self._get_param_iterator()),
            X,
            y,
            groups,
            fit_params,
            iid=self.iid,
            refit=self.refit,
            error_score=error_score,
            return_train_score=self.return_train_score,
            cache_cv=self.cache_cv,
            multimetric=multimetric,
        )
        self.dask_graph_ = dsk
        self.n_splits_ = n_splits

        n_jobs = _normalize_n_jobs(self.n_jobs)
        scheduler = dask.base.get_scheduler(scheduler=self.scheduler)

        if not scheduler:
            scheduler = dask.threaded.get
        if scheduler is dask.threaded.get and n_jobs == 1:
            scheduler = dask.local.get_sync

        out = scheduler(dsk, keys, num_workers=n_jobs)

        results = handle_deprecated_train_score(out[0], self.return_train_score)
        self.cv_results_ = results

        if self.refit:
            if self.multimetric_:
                key = self.refit
            else:
                key = "score"
            self.best_index_ = np.flatnonzero(results["rank_test_{}".format(key)] == 1)[
                0
            ]

            self.best_estimator_ = out[1]

        return self