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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 _init_prior_posterior(self, rank, R, n_local_subj):
 """set prior for this subject Parameters rank : integer The rank of this process R : list of 2D arrays, element i has shape=[n_voxel, n_dim] Each element in the list contains the scanner coordinate matrix of fMRI data of one subject. n_local_subj : integer The number of subjects allocated to this process. Returns ------- HTFA Returns the instance itself. """


        if rank == 0:
            idx = np.random.choice(n_local_subj, 1)
            self.global_prior_, self.global_centers_cov,\
                self.global_widths_var = self.get_template(R[idx[0]])
            self.global_centers_cov_scaled =\
                self.global_centers_cov / float(self.n_subj)
            self.global_widths_var_scaled =\
                self.global_widths_var / float(self.n_subj)
            self.gather_posterior = np.zeros(self.n_subj * self.prior_size)
            self.global_posterior_ = np.zeros(self.prior_size)
        else:
            self.global_prior_ = np.zeros(self.prior_bcast_size)
            self.global_posterior_ = None
            self.gather_posterior = None
        return self