code: def forward(self, x, y):
      # Hidden units depend on two inputs: a one-hot encoded categorical variable x, and
      # a bernoulli variable y. Whereas x will typically be enumerated, y will be observed.
      # We apply x_to_hidden independently from y_to_hidden, then broadcast the non-enumerated
      # y part up to the enumerated x part in the + operation.
      x_onehot = y.new_zeros(
          x.shape[:-1] + (self.args.hidden_dim,)
      ).scatter_(-1, x, 1)
      y_conv = self.relu(
          self.conv(y.reshape(-1, 1, self.data_dim))
      ).reshape(y.shape[:-1] + (-1,))
      h = self.relu(
          self.x_to_hidden(x_onehot) + self.y_to_hidden(y_conv)
      )
      return self.hidden_to_logits(h)
  
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 the TonesGenerator obj's underlying member method code with example usage: obj.forward(x, y) and returns: self