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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 _sssrm(self, data_align, data_sup, labels):
 """Block-Coordinate Descent algorithm for fitting SS-SRM. Parameters data_align : list of 2D arrays, element i has shape=[voxels_i, n_align] Each element in the list contains the fMRI data for alignment of one subject. There are n_align samples for each subject. data_sup : list of 2D arrays, element i has shape=[voxels_i, samples_i] Each element in the list contains the fMRI data of one subject for the classification task. labels : list of arrays of int, element i has shape=[samples_i] Each element in the list contains the labels for the data samples in data_sup. Returns ------- w : list of array, element i has shape=[voxels_i, features] The orthogonal transforms (mappings) :math:`W_i` for each subject. s : array, shape=[features, samples] The shared response. """

        classes = self.classes_.size

        # Initialization:
        self.random_state_ = np.random.RandomState(self.rand_seed)
        random_states = [
            np.random.RandomState(self.random_state_.randint(2**32))
            for i in range(len(data_align))]
        # Set Wi's to a random orthogonal voxels by TRs
        w, _ = srm._init_w_transforms(data_align, self.features, random_states)

        # Initialize the shared response S
        s = SSSRM._compute_shared_response(data_align, w)

        # Initialize theta and bias
        theta, bias = self._update_classifier(data_sup, labels, w, classes)

        # calculate and print the objective function
        if logger.isEnabledFor(logging.INFO):
            objective = self._objective_function(data_align, data_sup, labels,
                                                 w, s, theta, bias)
            logger.info('Objective function %f' % objective)

        # Main loop:
        for iteration in range(self.n_iter):
            logger.info('Iteration %d' % (iteration + 1))

            # Update the mappings Wi
            w = self._update_w(data_align, data_sup, labels, w, s, theta, bias)

            # Output the objective function
            if logger.isEnabledFor(logging.INFO):
                objective = self._objective_function(data_align, data_sup,
                                                     labels, w, s, theta, bias)
                logger.info('Objective function after updating Wi  %f'
                            % objective)

            # Update the shared response S
            s = SSSRM._compute_shared_response(data_align, w)

            # Output the objective function
            if logger.isEnabledFor(logging.INFO):
                objective = self._objective_function(data_align, data_sup,
                                                     labels, w, s, theta, bias)
                logger.info('Objective function after updating S   %f'
                            % objective)

            # Update the MLR classifier, theta and bias
            theta, bias = self._update_classifier(data_sup, labels, w, classes)

            # Output the objective function
            if logger.isEnabledFor(logging.INFO):
                objective = self._objective_function(data_align, data_sup,
                                                     labels, w, s, theta, bias)
                logger.info('Objective function after updating MLR %f'
                            % objective)

        return w, s, theta, bias