code: def model_1(
      sequences, lengths, args, batch_size=None, include_prior=True
  ):
      # Sometimes it is safe to ignore jit warnings. Here we use the
      # pyro.util.ignore_jit_warnings context manager to silence warnings about
      # conversion to integer, since we know all three numbers will be the same
      # across all invocations to the model.
      with ignore_jit_warnings():
          num_sequences, max_length, data_dim = map(
              int, sequences.shape
          )
          assert lengths.shape == (num_sequences,)
          assert lengths.max() <= max_length
      with handlers.mask(mask=include_prior):
          probs_x = pyro.sample(
              "probs_x",
              dist.Dirichlet(
                  0.9 * torch.eye(args.hidden_dim) + 0.1
              ).to_event(1),
          )
          probs_y = pyro.sample(
              "probs_y",
              dist.Beta(0.1, 0.9)
              .expand([args.hidden_dim, data_dim])
              .to_event(2),
          )
      tones_plate = pyro.plate("tones", data_dim, dim=-1)
      # We subsample batch_size items out of num_sequences items. Note that since
      # we're using dim=-1 for the notes plate, we need to batch over a different
      # dimension, here dim=-2.
      with pyro.plate(
          "sequences", num_sequences, batch_size, dim=-2
      ) as batch:
          lengths = lengths[batch]
          x = 0
          # If we are not using the jit, then we can vary the program structure
          # each call by running for a dynamically determined number of time
          # steps, lengths.max(). However if we are using the jit, then we try to
          # keep a single program structure for all minibatches; the fixed
          # structure ends up being faster since each program structure would
          # need to trigger a new jit compile stage.
          for t in pyro.markov(
              range(max_length if args.jit else lengths.max())
          ):
              with handlers.mask(mask=(t < lengths).unsqueeze(-1)):
                  x = pyro.sample(
                      "x_{}".format(t),
                      dist.Categorical(probs_x[x]),
                      infer={"enumerate": "parallel"},
                  )
                  with tones_plate:
                      pyro.sample(
                          "y_{}".format(t),
                          dist.Bernoulli(probs_y[x.squeeze(-1)]),
                          obs=sequences[batch, t],
                      )
  
imports: import argparse
  import functools
  import logging
  import sys
  import torch
  import torch.nn
  from torch.distributions import constraints
  from pyro.contrib.examples import polyphonic_data_loader
  from pyro.infer.autoguide import AutoDelta
  from pyro.ops.indexing import Vindex
  from pyro.util import ignore_jit_warnings
  from pyroapi import distributions
  from pyroapi import handlers, infer, optim, pyro, pyro_backend
label_desc: Use image node_id 2 for calling a global function with example usage: model_1(sequences, lengths, args, batch_size, include_prior) without return types