code: def test_bincount():
      """Test that bincount works in deterministic setting on GPU."""
      torch.use_deterministic_algorithms(True)
  
      x = torch.randint(10, size=(100,))
      # uses custom implementation
      res1 = _bincount(x, minlength=10)
  
      torch.use_deterministic_algorithms(False)
  
      # uses torch.bincount
      res2 = _bincount(x, minlength=10)
  
      # explicit call to make sure, that res2 is not by accident using our manual implementation
      res3 = torch.bincount(x, minlength=10)
  
      # check for correctness
      assert torch.allclose(res1, res2)
      assert torch.allclose(res1, res3)
  
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 8 for calling a global function with example usage: test_bincount() without return types