code: def test_combined_loader_sequence_with_map_and_iterable(lengths):
      class MyIterableDataset(IterableDataset):
          def __init__(self, size: int = 10):
              self.size = size
  
          def __iter__(self):
              self.sampler = SequentialSampler(range(self.size))
              self.iter_sampler = iter(self.sampler)
              return self
  
          def __next__(self):
              return next(self.iter_sampler)
  
      class MyMapDataset(Dataset):
          def __init__(self, size: int = 10):
              self.size = size
  
          def __getitem__(self, index):
              return index
  
          def __len__(self):
              return self.size
  
      x, y = lengths
      loaders = [
          DataLoader(MyIterableDataset(x)),
          DataLoader(MyMapDataset(y)),
      ]
      dataloader = CombinedLoader(loaders, mode="max_size_cycle")
      seen = sum(1 for _ in dataloader)
      assert seen == max(x, y)
  
imports: import math
  import pickle
  from typing import Any, NamedTuple, Sequence, get_args
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.utilities.combined_loader import _LITERAL_SUPPORTED_MODES, _SUPPORTED_MODES, CombinedLoader, _MaxSize, _MaxSizeCycle, _MinSize, _Sequential
  from torch import Tensor
  from torch.utils._pytree import tree_flatten
  from torch.utils.data import DataLoader, TensorDataset
  from torch.utils.data.dataset import Dataset, IterableDataset
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import RandomSampler, SequentialSampler
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 13 for calling a global function with example usage: test_combined_loader_sequence_with_map_and_iterable(lengths) and returns: self, next, index, self