code: def test_that_precision_string_repr_doesnt_throw_errors(y_ypred):
      predicted, true = y_ypred
      predicted = (
          predicted.softmax(-1) + torch.randn_like(predicted) / 100
      )
      precision = Precision(num_classes=predicted.size(1), average=True)
      precision.update(predicted, true)
      assert "±" in str(precision)
  
imports: import os
  from pathlib import Path
  import numpy
  import pytest
  import shutil
  import torch
  from hypothesis import given
  from torch_hypothesis import classification_logits_and_labels
  from baal.utils.metrics import Loss, Accuracy, Precision, ECE, ClassificationReport, PRAuC, ECE_PerCLs
label_desc: Use image node_id 6 for calling a global function with example usage: test_that_precision_string_repr_doesnt_throw_errors(y_ypred) without return types