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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 _objective_function(X, W, R, S, gamma):
 """Evaluate the objective function. .. math:: \\sum_{i=1}^{N} 1/2 \\| X_i - W_i R - S_i \\|_F^2 .. math:: + /\\gamma * \\|S_i\\|_1 Parameters X : list of array, element i has shape=[voxels_i, timepoints] Each element in the list contains the fMRI data for alignment of one subject. W : list of array, element i has shape=[voxels_i, features] The orthogonal transforms (mappings) :math:`W_i` for each subject. R : array, shape=[features, timepoints] The shared response. S : list of array, element i has shape=[voxels_i, timepoints] The individual component :math:`S_i` for each subject. gamma : float, default: 1.0 Regularization parameter for the sparseness of the individual components. Returns ------- func : float The RSRM objective function evaluated on the parameters to this function. """

        subjs = len(X)
        func = .0
        for i in range(subjs):
            func += 0.5 * np.sum((X[i] - W[i].dot(R) - S[i])**2) \
                    + gamma * np.sum(np.abs(S[i]))
        return func