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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 analyze(self, M_c, T, X_L, X_D, seed, kernel_list=(), n_steps=1, c=(), r=(), max_iterations=-1, max_time=-1, do_diagnostics=False, diagnostics_every_N=1, ROW_CRP_ALPHA_GRID=(), COLUMN_CRP_ALPHA_GRID=(), S_GRID=(), MU_GRID=(), N_GRID=31, do_timing=False, CT_KERNEL=0, progress=None, ):
 """Evolve the latent state by running MCMC transition kernels. :param seed: The random seed :type seed: int :param M_c: The column metadata :type M_c: dict :param T: The data table in mapped representation (all floats, generated by data_utils.read_data_objects) :param X_L: the latent variables associated with the latent state :type X_L: dict :param X_D: the particular cluster assignments of each row in each view :type X_D: list of lists :param kernel_list: names of the MCMC transition kernels to run :type kernel_list: list of strings :param n_steps: the number of times to run each MCMC transition kernel :type n_steps: int :param c: the (global) column indices to run MCMC transition kernels on :type c: list of ints :param r: the (global) row indices to run MCMC transition kernels on :type r: list of ints :param max_iterations: the maximum number of times ot run each MCMC transition kernel. Applicable only if max_time != -1. :type max_iterations: int :param max_time: the maximum amount of time (seconds) to run MCMC transition kernels for before stopping to return progress :type max_time: float :param progress: a function accepting (n_steps, max_time, step_idx, elapsed_secs, end=None) where `n_steps` is the total number of transition steps, `max_time` is the timeout in secods, `step_idx` is number of transitions so far, `elapsed_secs` is the amount of time so far, and `end=None` is an optional kwarg for indicating the analysis has been completed. For example, `progress` may be used to print a progress bar to standard out. :type progress: function pointer. :returns: X_L, X_D -- the evolved latent state """

        if n_steps <= 0:
            raise ValueError("You must do at least one analyze step.")

        if CT_KERNEL not in [0, 1]:
            raise ValueError("CT_KERNEL must be 0 (Gibbs) or 1 (MH)")

        if do_timing:
            # Diagnostics and timing are exclusive.
            do_diagnostics = False

        diagnostic_func_dict, reprocess_diagnostics_func = \
            do_diagnostics_to_func_dict(do_diagnostics)

        X_L_list, X_D_list, was_multistate = su.ensure_multistate(X_L, X_D)

        arg_tuples = self.get_analyze_arg_tuples(
            M_c,
            T,
            X_L_list,
            X_D_list,
            kernel_list,
            n_steps,
            c,
            r,
            max_iterations,
            max_time,
            diagnostic_func_dict,
            diagnostics_every_N,
            ROW_CRP_ALPHA_GRID,
            COLUMN_CRP_ALPHA_GRID,
            S_GRID,
            MU_GRID,
            N_GRID,
            do_timing,
            CT_KERNEL,
            progress,
            make_get_next_seed(seed))

        chain_tuples = self.mapper(self.do_analyze, arg_tuples)

        X_L_list, X_D_list, diagnostics_dict_list = zip(*chain_tuples)

        if do_timing:
            timing_list = diagnostics_dict_list

        if not was_multistate:
            X_L_list, X_D_list = X_L_list[0], X_D_list[0]
        ret_tuple = X_L_list, X_D_list

        if diagnostic_func_dict is not None:
            diagnostics_dict = munge_diagnostics(diagnostics_dict_list)
            if reprocess_diagnostics_func is not None:
                diagnostics_dict = reprocess_diagnostics_func(diagnostics_dict)
            ret_tuple = ret_tuple + (diagnostics_dict, )

        if do_timing:
            ret_tuple = ret_tuple + (timing_list, )

        return ret_tuple