class_docstr: This PyTorch Module encapsulates the model as well as the
  variational distribution (the guide) for the Deep Markov Model
class_docstr_tok: ["This","PyTorch","Module","encapsulates","the","model","as","well","as","the","variational","distribution","(","the","guide",")","for","the","Deep","Markov","Model"]
code: def guide(
      self,
      mini_batch,
      mini_batch_reversed,
      mini_batch_mask,
      mini_batch_seq_lengths,
      annealing_factor=1.0,
  ):
      # this is the number of time steps we need to process in the mini-batch
      T_max = mini_batch.size(1)
      # register all PyTorch (sub)modules with pyro
      pyro.module("dmm", self)
  
      # if on gpu we need the fully broadcast view of the rnn initial state
      # to be in contiguous gpu memory
      h_0_contig = self.h_0.expand(
          1, mini_batch.size(0), self.rnn.hidden_size
      ).contiguous()
      # push the observed x's through the rnn;
      # rnn_output contains the hidden state at each time step
      rnn_output, _ = self.rnn(mini_batch_reversed, h_0_contig)
      # reverse the time-ordering in the hidden state and un-pack it
      rnn_output = poly.pad_and_reverse(
          rnn_output, mini_batch_seq_lengths
      )
      # set z_prev = z_q_0 to setup the recursive conditioning in q(z_t |...)
      z_prev = self.z_q_0.expand(mini_batch.size(0), self.z_q_0.size(0))
  
      # we enclose all the sample statements in the guide in a plate.
      # this marks that each datapoint is conditionally independent of the others.
      with pyro.plate("z_minibatch", len(mini_batch)):
          # sample the latents z one time step at a time
          # we wrap this loop in pyro.markov so that TraceEnum_ELBO can use multiple samples from the guide at each z
          for t in pyro.markov(range(1, T_max + 1)):
              # the next two lines assemble the distribution q(z_t | z_{t-1}, x_{t:T})
              z_loc, z_scale = self.combiner(
                  z_prev, rnn_output[:, t - 1, :]
              )
  
              # if we are using normalizing flows, we apply the sequence of transformations
              # parameterized by self.iafs to the base distribution defined in the previous line
              # to yield a transformed distribution that we use for q(z_t|...)
              if len(self.iafs) > 0:
                  z_dist = TransformedDistribution(
                      dist.Normal(z_loc, z_scale), self.iafs
                  )
                  assert z_dist.event_shape == (self.z_q_0.size(0),)
                  assert z_dist.batch_shape[-1:] == (len(mini_batch),)
              else:
                  z_dist = dist.Normal(z_loc, z_scale)
                  assert z_dist.event_shape == ()
                  assert z_dist.batch_shape[-2:] == (
                      len(mini_batch),
                      self.z_q_0.size(0),
                  )
  
              # sample z_t from the distribution z_dist
              with pyro.poutine.scale(scale=annealing_factor):
                  if len(self.iafs) > 0:
                      # in output of normalizing flow, all dimensions are correlated (event shape is not empty)
                      z_t = pyro.sample(
                          "z_%d" % t,
                          z_dist.mask(mini_batch_mask[:, t - 1]),
                      )
                  else:
                      # when no normalizing flow used, ".to_event(1)" indicates latent dimensions are independent
                      z_t = pyro.sample(
                          "z_%d" % t,
                          z_dist.mask(
                              mini_batch_mask[:, t - 1 : t]
                          ).to_event(1),
                      )
              # the latent sampled at this time step will be conditioned upon in the next time step
              # so keep track of it
              z_prev = z_t
  
imports: import argparse
  import logging
  import time
  from os.path import exists
  import numpy
  import torch
  import torch.nn
  import pyro
  import pyro.contrib.examples.polyphonic_data_loader
  import pyro.distributions
  import pyro.poutine
  from pyro.distributions import TransformedDistribution
  from pyro.distributions.transforms import affine_autoregressive
  from pyro.infer import SVI, JitTrace_ELBO, Trace_ELBO, TraceEnum_ELBO, TraceTMC_ELBO, config_enumerate
  from pyro.optim import ClippedAdam
label_desc: Use image node_id 3 for calling the DMM obj's underlying member method code with example usage: obj.guide(mini_batch, mini_batch_reversed, mini_batch_mask, mini_batch_seq_lengths, annealing_factor) without return types