code: def test_deep_to_jit(shape):
      loc = torch.tensor(0.0, requires_grad=True)
      scale = torch.tensor(1.0, requires_grad=True)
      data = torch.randn(5, 1, 1)
  
      def fn(loc, scale, data):
          d = dist.Normal(loc, scale, validate_args=False)
          if shape is not None:
              d = d.expand(shape)
          d = deep_to(d)
          # do some arbitrary math
          y = (data - d.rsample()) / 2
          return d.log_prob(y) + d.entropy()
  
      jit_fn = torch.jit.trace(
          fn, (loc, scale, data), check_trace=False
      )
  
      # Check value.
      torch.manual_seed(12345)
      expected = fn(loc, scale, data)
      torch.manual_seed(12345)
      actual = jit_fn(loc, scale, data)
      assert_equal(actual, expected)
  
      # Check grads.
      actual_grads = grad(actual.sum(), [loc, scale], retain_graph=True)
      expected_grads = grad(actual.sum(), [loc, scale])
      for a, e in zip(actual_grads, expected_grads):
          assert_equal(a, e)
  
imports: import weakref
  import numpy
  import pytest
  import torch
  from torch.autograd import grad
  import pyro.distributions
  from pyro.distributions import constraints
  from pyro.distributions.util import broadcast_shape, deep_to, detach, sum_leftmost, sum_rightmost, weakmethod
  from pyro.nn.module import PyroModule, PyroParam, PyroSample
  from tests.common import assert_equal
label_desc: Use image node_id 16 for calling a global function with example usage: test_deep_to_jit(shape) and returns: unknown