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",")","`"]
code: def __init__(self, input_dim, z_dim, emission_dim):
      super().__init__()
      # initialize the three linear transformations used in the neural network
      self.lin_z_to_hidden = nn.Linear(z_dim, emission_dim)
      self.lin_hidden_to_hidden = nn.Linear(emission_dim, emission_dim)
      self.lin_hidden_to_input = nn.Linear(emission_dim, input_dim)
      # initialize the two non-linearities used in the neural network
      self.relu = nn.ReLU()
  
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 1 to create a new Emitter object from inherited base classes: nn with example: obj = Emitter(input_dim, z_dim, emission_dim)