code: def _test_state_dict_is_synced(rank, tmpdir):
      class DummyCatMetric(Metric):
          full_state_update = True
  
          def __init__(self) -> None:
              super().__init__()
              self.add_state(
                  "x", torch.tensor(0), dist_reduce_fx=torch.sum
              )
              self.add_state(
                  "c", torch.tensor(0), dist_reduce_fx=torch.sum
              )
  
          def update(self, x):
              self.x += x
              self.c += 1
  
          def compute(self):
              return self.x // self.c
  
          def __repr__(self) -> str:
              return f"DummyCatMetric(x={self.x}, c={self.c})"
  
      metric = DummyCatMetric()
      metric.persistent(True)
  
      def verify_metric(metric, i, world_size):
          state_dict = metric.state_dict()
          exp_sum = i * (i + 1) / 2
          assert state_dict["x"] == exp_sum * world_size
          assert metric.x == exp_sum * world_size
          assert metric.c == (i + 1) * world_size
          assert state_dict["c"] == metric.c
  
      steps = 5
      for i in range(steps):
          if metric._is_synced:
              with pytest.raises(
                  TorchMetricsUserError,
                  match="The Metric shouldn't be synced when performing",
              ):
                  metric(i)
  
              metric.unsync()
  
          metric(i)
  
          verify_metric(metric, i, 1)
  
          metric.sync()
          assert metric._is_synced
  
          with pytest.raises(
              TorchMetricsUserError,
              match="The Metric has already been synced.",
          ):
              metric.sync()
  
          verify_metric(metric, i, 2)
  
          metric.unsync()
          assert not metric._is_synced
  
          with pytest.raises(
              TorchMetricsUserError,
              match="The Metric has already been un-synced.",
          ):
              metric.unsync()
  
          with metric.sync_context():
              assert metric._is_synced
              verify_metric(metric, i, 2)
  
          with metric.sync_context(should_unsync=False):
              assert metric._is_synced
              verify_metric(metric, i, 2)
  
          assert metric._is_synced
  
          metric.unsync()
          assert not metric._is_synced
  
          metric.sync()
          cache = metric._cache
          metric._cache = None
  
          with pytest.raises(
              TorchMetricsUserError,
              match="The internal cache should exist to unsync the Metric.",
          ):
              metric.unsync()
  
          metric._cache = cache
  
      def reload_state_dict(state_dict, expected_x, expected_c):
          metric = DummyCatMetric()
          metric.load_state_dict(state_dict)
          assert metric.x == expected_x
          assert metric.c == expected_c
  
      reload_state_dict(deepcopy(metric.state_dict()), 20, 10)
  
      metric.unsync()
      reload_state_dict(deepcopy(metric.state_dict()), 10, 5)
  
      metric.sync()
  
      filepath = os.path.join(tmpdir, f"weights-{rank}.pt")
  
      torch.save(metric.state_dict(), filepath)
  
      metric.unsync()
      with metric.sync_context():
          torch.save(metric.state_dict(), filepath)
  
imports: import os
  import sys
  from copy import deepcopy
  from functools import partial
  import pytest
  import torch
  from torch import tensor
  from torchmetrics import Metric
  from torchmetrics.utilities.distributed import gather_all_tensors
  from torchmetrics.utilities.exceptions import TorchMetricsUserError
  from unittests import NUM_PROCESSES
  from unittests.helpers import seed_all
  from unittests.helpers.testers import DummyListMetric, DummyMetric, DummyMetricSum
label_desc: Use image node_id 10 for calling a global function with example usage: _test_state_dict_is_synced(rank, tmpdir) and returns: unknown, str, self, str, self, str