code: def test_binary_wrong_inputs():
      pr = Precision()
  
      assert pr._updated is False
      with pytest.raises(
          ValueError,
          match=r"For binary cases, y must be comprised of 0's and 1's",
      ):
          # y has not only 0 or 1 values
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.arange(0, 10).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.raises(
          ValueError,
          match=r"For binary cases, y_pred must be comprised of 0's and 1's",
      ):
          # y_pred values are not thresholded to 0, 1 values
          pr.update(
              (torch.rand(10), torch.randint(0, 2, size=(10,)).long())
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError, match=r"y must have shape of"):
          # incompatible shapes
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.randint(0, 2, size=(10, 5)).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError, match=r"y must have shape of"):
          # incompatible shapes
          pr.update(
              (
                  torch.randint(0, 2, size=(10, 5, 6)).long(),
                  torch.randint(0, 2, size=(10,)).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError, match=r"y must have shape of"):
          # incompatible shapes
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)).long(),
                  torch.randint(0, 2, size=(10, 5, 6)).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.warns(
          RuntimeWarning,
          match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False",
      ):
          pr = Precision(average=None)
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)).float(),
                  torch.randint(0, 2, size=(10,)),
              )
          )
  
      with pytest.warns(
          RuntimeWarning,
          match="`y` and `y_pred` should be of dtype long when entry type is binary and average!=False",
      ):
          pr = Precision(average=None)
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)),
                  torch.randint(0, 2, size=(10,)).float(),
              )
          )
  
imports: import warnings
  import pytest
  import torch
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import precision_score
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Precision
label_desc: Use image node_id 3 for calling a global function with example usage: test_binary_wrong_inputs() without return types