code: def test_param_group_scheduler_asserts():
      t1 = torch.zeros([1], requires_grad=True)
      t2 = torch.zeros([1], requires_grad=True)
      optimizer = torch.optim.SGD(
          [{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}]
      )
  
      lr_scheduler1 = LinearCyclicalScheduler(
          optimizer,
          "lr",
          param_group_index=0,
          start_value=1.0,
          end_value=0.0,
          cycle_size=10,
      )
      lr_scheduler2 = LinearCyclicalScheduler(
          optimizer,
          "lr",
          param_group_index=1,
          start_value=1.0,
          end_value=0.0,
          cycle_size=10,
      )
  
      with pytest.raises(
          TypeError, match=r"Argument schedulers should be a list/tuple"
      ):
          ParamGroupScheduler(schedulers=None, names=["a", "b", "c"])
  
      with pytest.raises(
          ValueError,
          match=r"Argument schedulers should be a list/tuple of parameter schedulers",
      ):
          ParamGroupScheduler(
              schedulers=[0, 1, 2], names=["a", "b", "c"]
          )
  
      with pytest.raises(
          ValueError,
          match=r"Argument schedulers should be a list/tuple of parameter schedulers",
      ):
          ParamGroupScheduler(
              schedulers=[lr_scheduler1, "2"], names=["a", "b"]
          )
  
      with pytest.raises(
          TypeError, match=r"Argument names should be a list/tuple"
      ):
          ParamGroupScheduler(
              schedulers=[lr_scheduler1, lr_scheduler2], names="ab"
          )
  
      with pytest.raises(
          ValueError,
          match=r"Argument names should be a list/tuple of parameter scheduler's names",
      ):
          ParamGroupScheduler(
              schedulers=[lr_scheduler1, lr_scheduler2], names=[1, 2]
          )
  
      with pytest.raises(ValueError, match=r"\d should be equal \d"):
          ParamGroupScheduler(
              schedulers=[lr_scheduler1, lr_scheduler2], names=["a"]
          )
  
      scheduler = ParamGroupScheduler(
          schedulers=[lr_scheduler1, lr_scheduler2], names=["a", "b"]
      )
      with pytest.raises(
          TypeError, match=r"Argument state_dict should be a dictionary"
      ):
          scheduler.load_state_dict(None)
  
      with pytest.raises(
          ValueError,
          match=r"Required state attribute 'schedulers' is absent in provided state_dict",
      ):
          scheduler.load_state_dict({"a": 1})
  
      with pytest.raises(
          ValueError,
          match=r"Input state_dict contains 0 state_dicts of param group schedulers",
      ):
          scheduler.load_state_dict({"schedulers": []})
  
      with pytest.raises(
          ValueError,
          match=r"Required state attribute 'schedulers' is absent in provided state_dict",
      ):
          scheduler.load_state_dict({})
  
      with pytest.raises(
          ValueError,
          match=r"Name of scheduler from input state dict does not "
          r"correspond to required one",
      ):
          scheduler.load_state_dict(
              {
                  "schedulers": [
                      ("a", lr_scheduler1.state_dict()),
                      ("bad_name", {}),
                  ]
              }
          )
  
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 21 for calling a global function with example usage: test_param_group_scheduler_asserts() without return types