code: def test_concat_scheduler_asserts():
      tensor = torch.zeros([1], requires_grad=True)
      optimizer = torch.optim.SGD([tensor], lr=0)
  
      scheduler_1 = LinearCyclicalScheduler(
          optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10
      )
      scheduler_2 = CosineAnnealingScheduler(
          optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10
      )
  
      with pytest.raises(
          TypeError, match=r"Argument schedulers should be a sequence"
      ):
          ConcatScheduler(schedulers=None, durations=[])
  
      with pytest.raises(
          ValueError,
          match=r"Argument schedulers should be of more than one parameter schedulers",
      ):
          ConcatScheduler(schedulers=[], durations=[])
  
      with pytest.raises(
          ValueError,
          match=r"Argument schedulers should be of more than one parameter schedulers",
      ):
          ConcatScheduler(schedulers=[scheduler_1], durations=[10])
  
      with pytest.raises(
          TypeError,
          match=r"Value at index 1 of schedulers should be a parameter scheduler",
      ):
          ConcatScheduler(schedulers=[scheduler_1, 12], durations=[10])
  
      with pytest.raises(
          ValueError,
          match=r"Incorrect number schedulers or duration values",
      ):
          ConcatScheduler(
              schedulers=[scheduler_1, scheduler_2], durations=[10, 5]
          )
  
      with pytest.raises(
          ValueError,
          match=r"Argument durations should be list/tuple of integers",
      ):
          ConcatScheduler(
              schedulers=[scheduler_1, scheduler_2, scheduler_2],
              durations=[15, 12.0],
          )
  
      with pytest.raises(
          TypeError, match=r"Argument durations should be list/tuple"
      ):
          ConcatScheduler(
              schedulers=[scheduler_1, scheduler_2], durations="abc"
          )
  
      with pytest.raises(
          TypeError,
          match=r"Argument param_names should be list or tuple",
      ):
          ConcatScheduler.simulate_values(
              num_events=123,
              schedulers=[scheduler_1, scheduler_2],
              durations=[15],
              param_names="abc",
          )
  
      with pytest.raises(
          ValueError,
          match=r"Argument param_names should be list or tuple of strings",
      ):
          ConcatScheduler.simulate_values(
              num_events=123,
              schedulers=[scheduler_1, scheduler_2],
              durations=[15],
              param_names=[1],
          )
  
      optimizer_2 = torch.optim.SGD([tensor], lr=0)
      scheduler_3 = CosineAnnealingScheduler(
          optimizer_2,
          "lr",
          start_value=0.0,
          end_value=1.0,
          cycle_size=10,
      )
  
      with pytest.raises(
          ValueError,
          match=r"schedulers should be related to same optimizer",
      ):
          ConcatScheduler([scheduler_1, scheduler_3], durations=[30])
  
      scheduler_4 = CosineAnnealingScheduler(
          optimizer,
          "lr2",
          start_value=0.0,
          end_value=1.0,
          cycle_size=10,
      )
  
      with pytest.raises(
          ValueError,
          match=r"schedulers should be related to same param_name",
      ):
          ConcatScheduler([scheduler_1, scheduler_4], durations=[30])
  
      with pytest.raises(
          ValueError,
          match=r"schedulers should be related to same optimizer",
      ):
          ConcatScheduler.simulate_values(
              3, [scheduler_1, scheduler_3], durations=[30]
          )
  
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 6 for calling a global function with example usage: test_concat_scheduler_asserts() without return types