code: def test_linear_scheduler_asserts():
      with pytest.raises(
          TypeError,
          match=r"Argument optimizer should be torch.optim.Optimizer",
      ):
          LinearCyclicalScheduler({}, "lr", 1, 0, cycle_size=0)
  
      tensor = torch.zeros([1], requires_grad=True)
      optimizer = torch.optim.SGD([tensor], lr=0.0)
  
      with pytest.raises(
          ValueError,
          match=r"Argument cycle_size should be positive and larger than 1",
      ):
          LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=0)
  
      with pytest.raises(
          ValueError,
          match=r"Argument cycle_size should be positive and larger than 1",
      ):
          LinearCyclicalScheduler(optimizer, "lr", 1, 0, cycle_size=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 2 for calling a global function with example usage: test_linear_scheduler_asserts() without return types