code: def test_extract_batch_size():
      """Tests the behavior of extracting the batch size."""
  
      def _check_warning_not_raised(data, expected):
          with no_warning_call(
              match="Trying to infer the `batch_size`"
          ):
              assert extract_batch_size(data) == expected
  
      def _check_warning_raised(data, expected):
          with pytest.warns(
              UserWarning,
              match=f"Trying to infer the `batch_size` .* we found is {expected}.",
          ):
              assert extract_batch_size(batch) == expected
          warning_cache.clear()
  
      def _check_error_raised(data):
          with pytest.raises(
              MisconfigurationException,
              match="We could not infer the batch_size",
          ):
              extract_batch_size(batch)
  
      @dataclass
      class CustomDataclass:
          a: Tensor
          b: Tensor
  
      # Warning not raised
      batch = torch.zeros(11, 10, 9, 8)
      _check_warning_not_raised(batch, 11)
  
      batch = {"test": torch.zeros(11, 10)}
      _check_warning_not_raised(batch, 11)
  
      batch = [torch.zeros(11, 10)]
      _check_warning_not_raised(batch, 11)
  
      batch = CustomDataclass(torch.zeros(11, 10), torch.zeros(11, 10))
      _check_warning_not_raised(batch, 11)
  
      batch = {"test": [{"test": [torch.zeros(11, 10)]}]}
      _check_warning_not_raised(batch, 11)
  
      # Warning raised
      batch = {
          "a": [torch.tensor(1), torch.tensor(2)],
          "b": torch.tensor([1, 2, 3, 4]),
      }
      _check_warning_raised(batch, 1)
  
      batch = CustomDataclass(torch.zeros(11, 10), torch.zeros(1))
      _check_warning_raised(batch, 11)
  
      batch = {
          "test": [{"test": [torch.zeros(11, 10), torch.zeros(10, 10)]}]
      }
      _check_warning_raised(batch, 11)
  
      batch = {
          "test": [{"test": [torch.zeros(10, 10), torch.zeros(11, 10)]}]
      }
      _check_warning_raised(batch, 10)
  
      batch = [
          {"test": torch.zeros(10, 10), "test_1": torch.zeros(11, 10)}
      ]
      _check_warning_raised(batch, 10)
  
      # Error raised
      batch = "test string"
      _check_error_raised(batch)
  
      data = {"test": ["some text"] * 7}
      _check_error_raised(data)
  
      class CustomBatch:
          def __init__(self):
              self.x = torch.randn(7, 2)
  
      data = CustomBatch()
      _check_error_raised(data)
  
imports: from dataclasses import dataclass
  from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  from lightning.fabric.utilities.data import _replace_dunder_methods
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import RandomDataset, RandomIterableDataset
  from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.data import _get_dataloader_init_args_and_kwargs, _update_dataloader, extract_batch_size, has_len_all_ranks, warning_cache
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning_utilities.test.warning import no_warning_call
  from torch import Tensor
  from torch.utils.data import BatchSampler, DataLoader, RandomSampler
label_desc: Use image node_id 1 for calling a global function with example usage: test_extract_batch_size() without return types