code: def test_grad_expand():
      def f(x, y):
          logger.debug("Inside f")
          loss = (x - y).pow(2).sum()
          return torch.autograd.grad(loss, [x, y], allow_unused=True)
  
      logger.debug("Compiling f")
      f = torch.jit.trace(
          f,
          (
              torch.zeros(2, requires_grad=True),
              torch.ones(1, requires_grad=True),
          ),
      )
      logger.debug("Invoking f")
      f(
          torch.zeros(2, requires_grad=True),
          torch.ones(1, requires_grad=True),
      )
      logger.debug("Invoking f")
      f(
          torch.zeros(2, requires_grad=True),
          torch.zeros(1, requires_grad=True),
      )
  
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 6 for calling a global function with example usage: test_grad_expand() and returns: torch