pyro/infer/traceenum_elbo.py [315:345]:
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        guide_trace.pack_tensors()
        model_trace.pack_tensors(guide_trace.plate_to_symbol)
        return model_trace, guide_trace

    def _get_traces(self, model, guide, args, kwargs):
        """
        Runs the guide and runs the model against the guide with
        the result packaged as a trace generator.
        """
        if self.max_plate_nesting == float('inf'):
            self._guess_max_plate_nesting(model, guide, args, kwargs)
        if self.vectorize_particles:
            guide = self._vectorized_num_particles(guide)
            model = self._vectorized_num_particles(model)

        # Enable parallel enumeration over the vectorized guide and model.
        # The model allocates enumeration dimensions after (to the left of) the guide,
        # accomplished by preserving the _ENUM_ALLOCATOR state after the guide call.
        guide_enum = EnumMessenger(first_available_dim=-1 - self.max_plate_nesting)
        model_enum = EnumMessenger()  # preserve _ENUM_ALLOCATOR state
        guide = guide_enum(guide)
        model = model_enum(model)

        q = queue.LifoQueue()
        guide = poutine.queue(guide, q,
                              escape_fn=iter_discrete_escape,
                              extend_fn=iter_discrete_extend)
        for i in range(1 if self.vectorize_particles else self.num_particles):
            q.put(poutine.Trace())
            while not q.empty():
                yield self._get_trace(model, guide, args, kwargs)
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pyro/infer/tracetmc_elbo.py [143:173]:
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        guide_trace.pack_tensors()
        model_trace.pack_tensors(guide_trace.plate_to_symbol)
        return model_trace, guide_trace

    def _get_traces(self, model, guide, args, kwargs):
        """
        Runs the guide and runs the model against the guide with
        the result packaged as a trace generator.
        """
        if self.max_plate_nesting == float('inf'):
            self._guess_max_plate_nesting(model, guide, args, kwargs)
        if self.vectorize_particles:
            guide = self._vectorized_num_particles(guide)
            model = self._vectorized_num_particles(model)

        # Enable parallel enumeration over the vectorized guide and model.
        # The model allocates enumeration dimensions after (to the left of) the guide,
        # accomplished by preserving the _ENUM_ALLOCATOR state after the guide call.
        guide_enum = EnumMessenger(first_available_dim=-1 - self.max_plate_nesting)
        model_enum = EnumMessenger()  # preserve _ENUM_ALLOCATOR state
        guide = guide_enum(guide)
        model = model_enum(model)

        q = queue.LifoQueue()
        guide = poutine.queue(guide, q,
                              escape_fn=iter_discrete_escape,
                              extend_fn=iter_discrete_extend)
        for i in range(1 if self.vectorize_particles else self.num_particles):
            q.put(poutine.Trace())
            while not q.empty():
                yield self._get_trace(model, guide, args, kwargs)
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