code: def model_0(
      sequences, lengths, args, batch_size=None, include_prior=True
  ):
      assert not torch._C._get_tracing_state()
      num_sequences, max_length, data_dim = sequences.shape
      with poutine.mask(mask=include_prior):
          # Our prior on transition probabilities will be:
          # stay in the same state with 90% probability; uniformly jump to another
          # state with 10% probability.
          probs_x = pyro.sample(
              "probs_x",
              dist.Dirichlet(
                  0.9 * torch.eye(args.hidden_dim) + 0.1
              ).to_event(1),
          )
          # We put a weak prior on the conditional probability of a tone sounding.
          # We know that on average about 4 of 88 tones are active, so we'll set a
          # rough weak prior of 10% of the notes being active at any one time.
          probs_y = pyro.sample(
              "probs_y",
              dist.Beta(0.1, 0.9)
              .expand([args.hidden_dim, data_dim])
              .to_event(2),
          )
      # In this first model we'll sequentially iterate over sequences in a
      # minibatch; this will make it easy to reason about tensor shapes.
      tones_plate = pyro.plate("tones", data_dim, dim=-1)
      for i in pyro.plate("sequences", len(sequences), batch_size):
          length = lengths[i]
          sequence = sequences[i, :length]
          x = 0
          for t in pyro.markov(range(length)):
              # On the next line, we'll overwrite the value of x with an updated
              # value. If we wanted to record all x values, we could instead
              # write x[t] = pyro.sample(...x[t-1]...).
              x = pyro.sample(
                  "x_{}_{}".format(i, t),
                  dist.Categorical(probs_x[x]),
                  infer={"enumerate": "parallel"},
              )
              with tones_plate:
                  pyro.sample(
                      "y_{}_{}".format(i, t),
                      dist.Bernoulli(probs_y[x.squeeze(-1)]),
                      obs=sequence[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 1 for calling a global function with example usage: model_0(sequences, lengths, args, batch_size, include_prior) without return types