class_docstr: Parameterizes the bernoulli observation likelihood `p(x_t | z_t)`
class_docstr_tok: ["Parameterizes","the","bernoulli","observation","likelihood","`","p","(","x_t","|","z_t",")","`"]
docstr: Given the latent z at a particular time step t we return the vector of
  probabilities `ps` that parameterizes the bernoulli distribution `p(x_t|z_t)`
docstr_tok: ["Given","the","latent","z","at","a","particular","time","step","t","we","return","the","vector","of","probabilities","`","ps","`","that","parameterizes","the","bernoulli","distribution","`","p","(","x_t|z_t",")","`"]
code: def forward(self, z_t):
      """
      Given the latent z at a particular time step t we return the vector of
      probabilities `ps` that parameterizes the bernoulli distribution `p(x_t|z_t)`
      """
      h1 = self.relu(self.lin_z_to_hidden(z_t))
      h2 = self.relu(self.lin_hidden_to_hidden(h1))
      ps = torch.sigmoid(self.lin_hidden_to_input(h2))
      return ps
  
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 2 for calling the Emitter obj's underlying member method code with example usage: obj.forward(z_t) and returns: ps