original_docstring: 
          Print the statistics of posterior samples collected during running this MCMC instance.
  
          :param float prob: the probability mass of samples within the credible interval.
          :param bool exclude_deterministic: whether or not print out the statistics
              at deterministic sites.
          
docstring: Print the statistics of posterior samples collected during running this MCMC instance.
docstring_tokens: Print the statistics of posterior samples collected during running this MCMC instance .
code: def print_summary(self, prob=0.9, exclude_deterministic=True):
          """
          Print the statistics of posterior samples collected during running this MCMC instance.
  
          :param float prob: the probability mass of samples within the credible interval.
          :param bool exclude_deterministic: whether or not print out the statistics
              at deterministic sites.
          """
          # Exclude deterministic sites by default
          sites = self._states[self._sample_field]
          if isinstance(sites, dict) and exclude_deterministic:
              state_sample_field = attrgetter(self._sample_field)(self._last_state)
              # XXX: there might be the case that state.z is not a dictionary but
              # its postprocessed value `sites` is a dictionary.
              # TODO: in general, when both `sites` and `state.z` are dictionaries,
              # they can have different key names, not necessary due to deterministic
              # behavior. We might revise this logic if needed in the future.
              if isinstance(state_sample_field, dict):
                  sites = {
                      k: v
                      for k, v in self._states[self._sample_field].items()
                      if k in state_sample_field
                  }
          print_summary(sites, prob=prob)
          extra_fields = self.get_extra_fields()
          if "diverging" in extra_fields:
              print(
                  "Number of divergences: {}".format(jnp.sum(extra_fields["diverging"]))
              )
code_tokens: def print_summary ( self , prob = 0.9 , exclude_deterministic = True ) : sites = self . _states [ self . _sample_field ] if isinstance ( sites , dict ) and exclude_deterministic : state_sample_field = attrgetter ( self . _sample_field ) ( self . _last_state ) if isinstance ( state_sample_field , dict ) : sites = { k : v for k , v in self . _states [ self . _sample_field ] . items ( ) if k in state_sample_field } print_summary ( sites , prob = prob ) extra_fields = self . get_extra_fields ( ) if "diverging" in extra_fields : print ( "Number of divergences: {}" . format ( jnp . sum ( extra_fields [ "diverging" ] ) ) )
short_docstring: Print the statistics of posterior samples collected during running this MCMC instance.
short_docstring_tokens: Print the statistics of posterior samples collected during running this MCMC instance .
comment: """
          Print the statistics of posterior samples collected during running this MCMC instance.
  
          :param float prob: the probability mass of samples within the credible interval.
          :param bool exclude_deterministic: whether or not print out the statistics
              at deterministic sites.
          """$SEP$# Exclude deterministic sites by default$SEP$# XXX: there might be the case that state.z is not a dictionary but$SEP$# its postprocessed value `sites` is a dictionary.$SEP$# TODO: in general, when both `sites` and `state.z` are dictionaries,$SEP$# they can have different key names, not necessary due to deterministic$SEP$# behavior. We might revise this logic if needed in the future.