code: def test_piecewiselinear_asserts():
      tensor = torch.zeros([1], requires_grad=True)
      optimizer = torch.optim.SGD([tensor], lr=0)
  
      with pytest.raises(
          TypeError,
          match=r"Argument milestones_values should be a list or tuple",
      ):
          PiecewiseLinear(optimizer, "lr", milestones_values=None)
  
      with pytest.raises(
          ValueError,
          match=r"Argument milestones_values should be with at least one value",
      ):
          PiecewiseLinear(optimizer, "lr", milestones_values=[])
  
      with pytest.raises(
          ValueError,
          match=r"Argument milestones_values should be a list of pairs",
      ):
          PiecewiseLinear(optimizer, "lr", milestones_values=[(0.5,)])
  
      with pytest.raises(
          ValueError,
          match=r"Argument milestones_values should be a list of pairs",
      ):
          PiecewiseLinear(
              optimizer, "lr", milestones_values=[(10, 0.5), (0.6,)]
          )
  
      with pytest.raises(
          ValueError, match=r"Milestones should be increasing integers"
      ):
          PiecewiseLinear(
              optimizer, "lr", milestones_values=[(10, 0.5), (5, 0.6)]
          )
  
      with pytest.raises(
          TypeError, match=r"Value of a milestone should be integer"
      ):
          PiecewiseLinear(optimizer, "lr", milestones_values=[(0.5, 1)])
  
imports: from unittest.mock import MagicMock, patch
  import numpy
  import pytest
  import torch
  from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, ExponentialLR, StepLR
  from ignite.engine import Engine, Events
  from ignite.handlers.param_scheduler import ConcatScheduler, CosineAnnealingScheduler, create_lr_scheduler_with_warmup, LinearCyclicalScheduler, LRScheduler, ParamGroupScheduler, ParamScheduler, PiecewiseLinear, ReduceLROnPlateauScheduler
  from tests.ignite.contrib.handlers import MockFP16DeepSpeedZeroOptimizer
label_desc: Use image node_id 14 for calling a global function with example usage: test_piecewiselinear_asserts() without return types