code: def test_masked_fill():
      def f(y, mask):
          return y.clone().masked_fill_(mask, 0.0)
  
      x = torch.tensor([-float("inf"), -1.0, 0.0, 1.0, float("inf")])
      y = x / x.unsqueeze(-1)
      mask = ~(y == y)
      jit_f = torch.jit.trace(f, (y, mask))
      assert_equal(jit_f(y, mask), f(y, mask))
  
      mask = torch.tensor([True, False, False, True, False, False])
      y = torch.tensor([1.5, 2.5, 3.5, 4.5, 5.5, 6.5])
      assert_equal(jit_f(y, mask), f(y, mask))
  
imports: import logging
  import warnings
  import pytest
  import torch
  from torch.autograd import grad
  from torch.distributions import constraints, kl_divergence
  import pyro
  import pyro.distributions
  import pyro.ops.jit
  import pyro.poutine
  from pyro.distributions.util import scale_and_mask
  from pyro.infer import SVI, JitTrace_ELBO, JitTraceEnum_ELBO, JitTraceGraph_ELBO, JitTraceMeanField_ELBO, Trace_ELBO, TraceEnum_ELBO, TraceGraph_ELBO, TraceMeanField_ELBO, TraceTMC_ELBO, infer_discrete
  from pyro.optim import Adam
  from pyro.poutine.indep_messenger import CondIndepStackFrame
  from pyro.util import ignore_jit_warnings
  from tests.common import assert_close, assert_equal
label_desc: Use image node_id 8 for calling a global function with example usage: test_masked_fill() and returns: y