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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 observe(self, C, obs_mesh_new, obs_vals_new, mean_under=None):
 """ Synchronizes self's observation status with C's. Values of observation are given by obs_vals. obs_mesh_new and obs_vals_new should already have """


        self.C = C
        self.obs_mesh = C.obs_mesh
        self.obs_len = C.obs_len

        self.Uo = C.Uo

        # Evaluate the underlying mean function on the new observation mesh.
        if mean_under is None:
            mean_under_new = C._mean_under_new(self, obs_mesh_new)
        else:
            mean_under_new = mean_under

        # If self hasn't been observed yet:
        if not self.observed:

            self.dev = (obs_vals_new - mean_under_new)

            self.reg_mat = C._unobs_reg(self)

        # If self has been observed already:
        elif len(obs_vals_new) > 0:

            # Rank of old observations.
            m_old = len(self.dev)

            # Deviation of new observation from mean without regard to old
            # observations.
            dev_new = (obs_vals_new - mean_under_new)

            # Again, basis covariances get special treatment.
            self.reg_mat = C._obs_reg(self, dev_new, m_old)

            # Stack deviations of old and new observations from unobserved
            # mean.
            self.dev = hstack((self.dev, dev_new))
        self.observed = True