code: def test_supports_tensor_lists(self, mode):
      with autograd_fallback_mode(mode):
          lib = self.get_lib()
          lib.define("foo(Tensor[] a) -> Tensor[]")
          op = self.get_op("foo")
  
          def foo_impl(a):
              x, y, z = a
              with torch.no_grad():
                  return x + y + z, x * y * z
  
          lib.impl("foo", foo_impl, "CPU")
          x = torch.randn(3, requires_grad=True)
          y = torch.randn(1, requires_grad=True)
          z = torch.randn(2, 1, requires_grad=True)
          a, b = op([x, y, z])
          with self._check_ctx(mode, mode_nothing_raises=True):
              torch.autograd.grad(
                  a,
                  (x, y, z),
                  torch.ones_like(a),
                  allow_unused=True,
                  retain_graph=True,
              )
          with self._check_ctx(mode, mode_nothing_raises=True):
              torch.autograd.grad(
                  b,
                  (x, y, z),
                  torch.ones_like(b),
                  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 18 for calling the TestAutogradFallback obj's underlying member method code with example usage: obj.test_supports_tensor_lists(mode) and returns: unknown,  unknown