code: def test_torch_pytree(self):
      # ensure torch.utils._pytree treats ModelOutput subclasses as nodes (and not leaves)
      # this is important for DistributedDataParallel gradient synchronization with static_graph=True
      import torch.utils._pytree as pytree
  
      x = ModelOutput({"a": 1.0, "c": 2.0})
      self.assertFalse(pytree._is_leaf(x))
  
      x = ModelOutputTest(a=1.0, c=2.0)
      self.assertFalse(pytree._is_leaf(x))
  
      expected_flat_outs = [1.0, 2.0]
      expected_tree_spec = pytree.TreeSpec(
          ModelOutputTest,
          (ModelOutputTest, ["a", "c"]),
          [pytree.LeafSpec(), pytree.LeafSpec()],
      )
  
      actual_flat_outs, actual_tree_spec = pytree.tree_flatten(x)
      self.assertEqual(expected_flat_outs, actual_flat_outs)
      self.assertEqual(expected_tree_spec, actual_tree_spec)
  
      unflattened_x = pytree.tree_unflatten(
          actual_flat_outs, actual_tree_spec
      )
      self.assertEqual(x, unflattened_x)
  
imports: import unittest
  from dataclasses import dataclass
  from typing import Optional
  from transformers.testing_utils import require_torch
  from transformers.utils import ModelOutput
label_desc: Use image node_id 9 for calling the ModelOutputTester obj's underlying member method code with example usage: obj.test_torch_pytree() without return types