code: def model_6(data, history, vectorized):
      x_dim = 3
      x_init = pyro.param(
          "x_init",
          lambda: torch.rand(x_dim),
          constraint=constraints.simplex,
      )
      x_trans = pyro.param(
          "x_trans",
          lambda: torch.rand((len(data) - 1, x_dim, x_dim)),
          constraint=constraints.simplex,
      )
      locs = pyro.param("locs", lambda: torch.rand(x_dim))
  
      x_prev = None
      markov_loop = (
          pyro.vectorized_markov(
              name="time", size=len(data), dim=-2, history=history
          )
          if vectorized
          else pyro.markov(range(len(data)), history=history)
      )
      for i in markov_loop:
          if isinstance(i, int) and i < 1:
              x_probs = x_init
          elif isinstance(i, int):
              x_probs = x_trans[i - 1, x_prev]
          else:
              x_probs = Vindex(x_trans)[(i - 1)[:, None], x_prev]
  
          x_curr = pyro.sample(
              "x_{}".format(i), dist.Categorical(x_probs)
          )
          with pyro.plate("tones", data.shape[-1], dim=-1):
              pyro.sample(
                  "y_{}".format(i),
                  dist.Normal(Vindex(locs)[..., x_curr], 1.0),
                  obs=data[i],
              )
          x_prev = x_curr
  
imports: import pytest
  import torch
  from pyroapi import pyro_backend
  from torch.distributions import constraints
  from pyro.ops.indexing import Vindex
label_desc: Use image node_id 7 for calling a global function with example usage: model_6(data, history, vectorized) without return types