def sample(
        self, iter, burn=0, thin=1, tune_interval=1000, tune_throughout=True,
        save_interval=None, burn_till_tuned=False, stop_tuning_after=5,
            verbose=0, progress_bar=True):
        """
        sample(iter, burn, thin, tune_interval, tune_throughout, save_interval, verbose, progress_bar)

        Initialize traces, run sampling loop, clean up afterward. Calls _loop.

        :Parameters:
          - iter : int
            Total number of iterations to do
          - burn : int
            Variables will not be tallied until this many iterations are complete, default 0
          - thin : int
            Variables will be tallied at intervals of this many iterations, default 1
          - tune_interval : int
            Step methods will be tuned at intervals of this many iterations, default 1000
          - tune_throughout : boolean
            If true, tuning will continue after the burnin period (True); otherwise tuning
            will halt at the end of the burnin period.
          - save_interval : int or None
            If given, the model state will be saved at intervals of this many iterations
          - verbose : boolean
          - progress_bar : boolean
            Display progress bar while sampling.
          - burn_till_tuned: boolean
            If True the Sampler would burn samples until all step methods are tuned.
            A tuned step methods is one that was not tuned for the last `stop_tuning_after` tuning intervals.
            The burn-in phase will have a minimum of 'burn' iterations but could be longer if
            tuning is needed. After the phase is done the sampler will run for another
            (iter - burn) iterations, and will tally the samples according to the 'thin' argument.
            This means that the total number of iteration is update throughout the sampling
            procedure.
            If burn_till_tuned is True it also overrides the tune_thorughout argument, so no step method
            will be tuned when sample are being tallied.
          - stop_tuning_after: int
            the number of untuned successive tuning interval needed to be reach in order for
            the burn-in phase to be done (If burn_till_tuned is True).
        """

        self.assign_step_methods(verbose=verbose)
        
        iter, burn, thin = np.floor([iter, burn, thin]).astype(int)

        if burn > iter:
            raise ValueError(
                'Burn interval cannot be larger than specified number of iterations.')

        self._n_tally = int(iter) - int(burn)
        if burn_till_tuned:
            self._stop_tuning_after = stop_tuning_after
            tune_throughout = False
            if verbose > 0:
                print(
                    "burn_til_tuned is True. tune_throughout is set to False")
            burn = int(max(burn, stop_tuning_after * tune_interval))
            iter = self._n_tally + burn

        self._iter = int(iter)
        self._burn = int(burn)
        self._thin = int(thin)
        self._tune_interval = int(tune_interval)
        self._tune_throughout = tune_throughout
        self._burn_till_tuned = burn_till_tuned
        self._save_interval = save_interval

        length = max(int(np.round((1.0 * iter - burn) / thin, 0)), 1)
        self.max_trace_length = length

        # Flags for tuning
        self._tuning = True
        self._tuned_count = 0

        # Progress bar
        self.pbar = None
        if not verbose and progress_bar:
            self.pbar = pbar(self._iter)

        # Run sampler
        Sampler.sample(self, iter, length, verbose)
sample(iter, burn, thin, tune_interval, tune_throughout, save_interval, verbose, progress_bar)

        Initialize traces, run sampling loop, clean up afterward. Calls _loop.

        :Parameters:
          - iter : int
            Total number of iterations to do
          - burn : int
            Variables will not be tallied until this many iterations are complete, default 0
          - thin : int
            Variables will be tallied at intervals of this many iterations, default 1
          - tune_interval : int
            Step methods will be tuned at intervals of this many iterations, default 1000
          - tune_throughout : boolean
            If true, tuning will continue after the burnin period (True); otherwise tuning
            will halt at the end of the burnin period.
          - save_interval : int or None
            If given, the model state will be saved at intervals of this many iterations
          - verbose : boolean
          - progress_bar : boolean
            Display progress bar while sampling.
          - burn_till_tuned: boolean
            If True the Sampler would burn samples until all step methods are tuned.
            A tuned step methods is one that was not tuned for the last `stop_tuning_after` tuning intervals.
            The burn-in phase will have a minimum of 'burn' iterations but could be longer if
            tuning is needed. After the phase is done the sampler will run for another
            (iter - burn) iterations, and will tally the samples according to the 'thin' argument.
            This means that the total number of iteration is update throughout the sampling
            procedure.
            If burn_till_tuned is True it also overrides the tune_thorughout argument, so no step method
            will be tuned when sample are being tallied.
          - stop_tuning_after: int
            the number of untuned successive tuning interval needed to be reach in order for
            the burn-in phase to be done (If burn_till_tuned is True).