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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 predict(self, X):
 """Classify the output for given data Parameters X : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject The number of voxels should be according to each subject at the moment of training the model. Returns ------- p: list of arrays, element i has shape=[samples_i] Predictions for each data sample. """

        # Check if the model exist
        if hasattr(self, 'w_') is False:
            raise NotFittedError("The model fit has not been run yet.")

        # Check the number of subjects
        if len(X) != len(self.w_):
            raise ValueError("The number of subjects does not match the one"
                             " in the model.")

        X_shared = self.transform(X)
        p = [None] * len(X_shared)
        for subject in range(len(X_shared)):
            sumexp, _, exponents = utils.sumexp_stable(
                self.theta_.T.dot(X_shared[subject]) + self.bias_)
            p[subject] = self.classes_[
                (exponents / sumexp[np.newaxis, :]).argmax(axis=0)]

        return p