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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 decision_function(self, X=None):
 """Output the decision value of the prediction. if X is not equal to self.test_raw_data\\_, i.e. predict is not called, first generate the test_data after getting the test_data, get the decision value via self.clf. if X is None, test_data\\_ is ready to be used Parameters X: Optional[list of tuple (data1, data2)] data1 and data2 are numpy array in shape [num_TRs, num_voxels] to be computed for correlation. default None, meaning that the data to be predicted have been processed in the fit method. Otherwise, X contains the activity data filtered by ROIs and prepared for correlation computation. len(X) is the number of test samples. if len(X) > 1: normalization is done on all test samples. Within list, all data1s must have the same num_voxels value, all data2s must have the same num_voxels value. Returns ------- confidence: the predictions confidence values of X, in shape [len(X),] """

        if X is not None and not self._is_equal_to_test_raw_data(X):
            for x in X:
                assert len(x) == 2, \
                    'there must be two parts for each correlation computation'
            X1, X2 = zip(*X)
            num_voxels1 = X1[0].shape[1]
            num_voxels2 = X2[0].shape[1]
            assert len(X1) == len(X2), \
                'the list lengths do not match'
            # make sure X1 always has more voxels
            if num_voxels1 < num_voxels2:
                X1, X2 = X2, X1
                num_voxels1, num_voxels2 = num_voxels2, num_voxels1
            assert self.num_features_ == num_voxels1 * num_voxels2, \
                'the number of features does not match the model'
            num_test_samples = len(X1)
            self.test_raw_data_ = X
            # generate the test_data first
            # correlation computation
            corr_data = self._prepare_corerelation_data(X1, X2)
            # normalization
            normalized_corr_data = \
                self._normalize_correlation_data(corr_data,
                                                 num_test_samples)
            # test data generation
            self.test_data_ = self._prepare_test_data(normalized_corr_data)
        confidence = self.clf.decision_function(self.test_data_)
        return confidence