code: def model_5(
      sequences, lengths, args, batch_size=None, include_prior=True
  ):
      with ignore_jit_warnings():
          num_sequences, max_length, data_dim = map(
              int, sequences.shape
          )
          assert lengths.shape == (num_sequences,)
          assert lengths.max() <= max_length
  
      # Initialize a global module instance if needed.
      global tones_generator
      if tones_generator is None:
          tones_generator = TonesGenerator(args, data_dim)
      pyro.module("tones_generator", tones_generator)
  
      with poutine.mask(mask=include_prior):
          probs_x = pyro.sample(
              "probs_x",
              dist.Dirichlet(
                  0.9 * torch.eye(args.hidden_dim) + 0.1
              ).to_event(1),
          )
      with pyro.plate(
          "sequences", num_sequences, batch_size, dim=-2
      ) as batch:
          lengths = lengths[batch]
          x = 0
          y = torch.zeros(data_dim)
          for t in pyro.markov(
              range(max_length if args.jit else lengths.max())
          ):
              with poutine.mask(mask=(t < lengths).unsqueeze(-1)):
                  x = pyro.sample(
                      "x_{}".format(t),
                      dist.Categorical(probs_x[x]),
                      infer={"enumerate": "parallel"},
                  )
                  # Note that since each tone depends on all tones at a previous time step
                  # the tones at different time steps now need to live in separate plates.
                  with pyro.plate(
                      "tones_{}".format(t), data_dim, dim=-1
                  ):
                      y = pyro.sample(
                          "y_{}".format(t),
                          dist.Bernoulli(logits=tones_generator(x, y)),
                          obs=sequences[batch, t],
                      )
  
imports: import argparse
  import logging
  import sys
  import torch
  import torch.nn
  from torch.distributions import constraints
  import pyro
  import pyro.contrib.examples.polyphonic_data_loader
  import pyro.distributions
  from pyro import poutine
  from pyro.infer import SVI, JitTraceEnum_ELBO, TraceEnum_ELBO, TraceTMC_ELBO
  from pyro.infer.autoguide import AutoDelta
  from pyro.ops.indexing import Vindex
  from pyro.optim import Adam
  from pyro.util import ignore_jit_warnings
label_desc: Use image node_id 6 for calling a global function with example usage: model_5(sequences, lengths, args, batch_size, include_prior) without return types