code: def test_post_autograd_returns_mix_of_requires_grad_tensors(
      self, mode
  ):
      with autograd_fallback_mode(mode):
          lib = self.get_lib()
          lib.define(
              "foo(Tensor a, Tensor b) -> (Tensor, Tensor, Tensor)"
          )
          op = self.get_op("foo")
  
          def foo_impl(a, b):
              with torch.no_grad():
                  x = a.clone()
                  z = b.clone()
              y = a * b
              return x, y, z
  
          lib.impl("foo", foo_impl, "CPU")
          a = torch.randn(3, requires_grad=True)
          b = torch.randn(3, requires_grad=True)
          x, y, z = op(a, b)
  
          with self._check_ctx(mode, mode_nothing_raises=True):
              torch.autograd.grad(
                  x,
                  (a, b),
                  torch.ones_like(x),
                  allow_unused=True,
                  retain_graph=True,
              )
  
          with self._check_ctx(mode, mode_nothing_raises=False):
              torch.autograd.grad(
                  y,
                  (a, b),
                  torch.ones_like(y),
                  allow_unused=True,
                  retain_graph=True,
              )
  
          with self._check_ctx(mode, mode_nothing_raises=True):
              torch.autograd.grad(
                  z,
                  (a, b),
                  torch.ones_like(z),
                  allow_unused=True,
                  retain_graph=True,
              )
  
imports: import contextlib
  import warnings
  import numpy
  import torch
  from torch.library import Library
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, TestCase
label_desc: Use image node_id 17 for calling the TestAutogradFallback obj's underlying member method code with example usage: obj.test_post_autograd_returns_mix_of_requires_grad_tensors(mode) and returns: x,  y,  z