code: def test_check_full_state_update_fn(capsys, metric_class, expected):
      """Test that the check function works as it should."""
      check_forward_full_state_property(
          metric_class=metric_class,
          input_args={
              "preds": torch.randn(1000),
              "target": torch.randn(1000),
          },
          num_update_to_compare=[10000],
          reps=5,
      )
      captured = capsys.readouterr()
      assert (
          f"Recommended setting `full_state_update={expected}`"
          in captured.out
      )
  
imports: import sys
  import numpy
  import pytest
  import torch
  from torch import tensor
  from torchmetrics.regression import MeanSquaredError, PearsonCorrCoef
  from torchmetrics.utilities import check_forward_full_state_property, rank_zero_debug, rank_zero_info, rank_zero_warn
  from torchmetrics.utilities.checks import _allclose_recursive
  from torchmetrics.utilities.data import _bincount, _cumsum, _flatten, _flatten_dict, select_topk, to_categorical, to_onehot
  from torchmetrics.utilities.distributed import class_reduce, reduce
  from torchmetrics.utilities.exceptions import TorchMetricsUserWarning
  from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_13
label_desc: Use image node_id 9 for calling a global function with example usage: test_check_full_state_update_fn(capsys, metric_class, expected) without return types