code: def test_logger_sync_dist(distributed_env, log_val):
      if log_val == "Accuracy":
          log_val = (
              Accuracy(task="binary") if _TM_GE_0_11 else Accuracy()
          )
  
      pl.trainer.connectors.logger_connector.result.warning_cache.clear()
  
      # self.log('bar', 0.5, ..., sync_dist=False)
      meta = _Metadata("foo", "bar")
      meta.sync = _Sync(_should=False)
      is_tensor = isinstance(log_val, Tensor)
  
      if not is_tensor:
          log_val.update(
              tensor([0, 1]), tensor([0, 0], dtype=torch.long)
          )
  
      result_metric = _ResultMetric(metadata=meta, is_tensor=is_tensor)
      result_metric.update(log_val, 10)
  
      warning_ctx = (
          pytest.warns
          if distributed_env and is_tensor
          else no_warning_call
      )
      patch_ctx = (
          mock.patch(
              "torch.distributed.is_initialized",
              return_value=distributed_env,
          )
          if isinstance(log_val, Tensor)
          else nullcontext()
      )
  
      with warning_ctx(
          PossibleUserWarning,
          match=r"recommended to use `self.log\('bar', ..., sync_dist=True\)`",
      ), patch_ctx:
          value = _ResultCollection._get_cache(
              result_metric, on_step=False
          )
      assert value == 0.5
  
imports: import os
  import pickle
  from contextlib import nullcontext, suppress
  from unittest import mock
  import lightning.pytorch
  import pytest
  import torch
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import OnExceptionCheckpoint
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.trainer.connectors.logger_connector.result import _Metadata, _ResultCollection, _ResultMetric, _Sync
  from lightning.pytorch.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_0_11
  from lightning_utilities.test.warning import no_warning_call
  from torch import Tensor, tensor
  from torch.nn import ModuleDict, ModuleList
  from torchmetrics import Metric, MetricCollection
  from torchmetrics.classification import Accuracy
  from tests_pytorch.core.test_results import spawn_launch
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 16 for calling a global function with example usage: test_logger_sync_dist(distributed_env, log_val) without return types