code: def _sanetize_batch_size(self, batch_size: int) -> int:
      if batch_size != 1:
          warning_cache.warn(
              "When using a meta-learning training_strategy, the batch_size should be set to 1. "
              "HINT: You can modify the `meta_batch_size` to 100 for example by doing "
              f"{type(self._task)}"
              + "(training_strategies_kwargs={'meta_batch_size': 100})"
          )
      return 1
  
imports: import functools
  import inspect
  import os
  from collections import defaultdict
  from functools import partial
  from typing import Any, Callable, List, Optional, Type
  import torch
  from lightning_utilities.core.rank_zero import WarningCache
  from pytorch_lightning import LightningModule
  from pytorch_lightning.strategies import DataParallelStrategy, DDPSpawnStrategy, DDPStrategy
  from pytorch_lightning.trainer.states import TrainerFn
  from torch import Tensor, nn
  from torch.utils.data import DataLoader, IterableDataset, Sampler
  import flash
  from flash.core.adapter import Adapter, AdapterTask
  from flash.core.data.io.input import DataKeys, InputBase
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.model import Task
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.compatibility import accelerator_connector
  from flash.core.utilities.imports import _LEARN2LEARN_AVAILABLE
  from flash.core.utilities.providers import _LEARN2LEARN
  from flash.core.utilities.stability import beta
  from flash.core.utilities.url_error import catch_url_error
  from flash.image.classification.integrations.learn2learn import TaskDataParallel, TaskDistributedDataParallel
label_desc: Use image node_id 13 for calling the Learn2LearnAdapter obj's underlying member method code with example usage: obj._sanetize_batch_size(batch_size) and returns: int