Small bug in BM25Transformer

[conan1024hao]

check_is_fitted in BM25Transformer does not work well with scikit-learn 1.0.1.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_1719/4208231121.py in <module>
----> 1 X_test = bm25.fit_transform(test_corpus)

/usr/local/lib64/python3.7/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
    432             fit_params_last_step = fit_params_steps[self.steps[-1][0]]
    433             if hasattr(last_step, "fit_transform"):
--> 434                 return last_step.fit_transform(Xt, y, **fit_params_last_step)
    435             else:
    436                 return last_step.fit(Xt, y, **fit_params_last_step).transform(Xt)

/usr/local/lib64/python3.7/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    845         if y is None:
    846             # fit method of arity 1 (unsupervised transformation)
--> 847             return self.fit(X, **fit_params).transform(X)
    848         else:
    849             # fit method of arity 2 (supervised transformation)

/tmp/ipykernel_1719/3233856808.py in transform(self, X, copy)
     80 
     81         if self.use_idf:
---> 82             check_is_fitted(self, '_idf_diag', 'idf vector is not fitted')
     83 
     84             expected_n_features = self._idf_diag.shape[0]

TypeError: check_is_fitted() takes from 1 to 2 positional arguments but 3 were given

After changing the code to the below one, the error was resolved.

check_is_fitted(self, attributes='_idf_diag', msg='idf vector is not fitted')