code: def _convert_dataset(
      self,
      trainer: "flash.Trainer",
      dataset: InputBase,
      ways: int,
      shots: int,
      queries: int,
      num_workers: int,
      num_task: int,
      epoch_length: int,
  ):
      if trainer is None:
          raise ValueError(
              "The Learn2Learn integration requires the `Trainer` to be passed to the `process_*_dataset` method."
          )
  
      if isinstance(dataset, InputBase):
          metadata = getattr(dataset, "data", None)
          if metadata is None or (
              metadata is not None
              and not isinstance(dataset.data, list)
          ):
              raise TypeError(
                  "Only dataset built out of metadata is supported."
              )
  
          labels_to_indices = self._labels_to_indices(dataset.data)
  
          if len(labels_to_indices) < ways:
              raise ValueError(
                  "Provided `ways` should be lower or equal to number of classes within your dataset."
              )
  
          if min(
              len(indice) for indice in labels_to_indices.values()
          ) < (shots + queries):
              raise ValueError(
                  "Provided `shots + queries` should be lower than the lowest number of sample per class."
              )
  
          # convert the dataset to MetaDataset
          dataset = l2l.data.MetaDataset(
              dataset,
              indices_to_labels=None,
              labels_to_indices=labels_to_indices,
          )
  
          transform_fn = (
              self.default_transforms_fn or self._default_transform
          )
  
          taskset = l2l.data.TaskDataset(
              dataset=dataset,
              task_transforms=transform_fn(
                  dataset, ways=ways, shots=shots, queries=queries
              ),
              num_tasks=num_task,
              task_collate=self._identity_task_collate_fn,
          )
  
      is_ddp_or_ddp_spawn = isinstance(
          trainer.strategy, (DDPStrategy, DDPSpawnStrategy)
      )
      if is_ddp_or_ddp_spawn:
          # when running in a distributed data parallel way,
          # we are actually sampling one task per device.
          dataset = TaskDistributedDataParallel(
              taskset=taskset,
              global_rank=trainer.global_rank,
              world_size=trainer.world_size,
              num_workers=num_workers,
              epoch_length=epoch_length,
              seed=os.getenv("PL_GLOBAL_SEED", self.seed),
              requires_divisible=trainer.training,
          )
          self.trainer.accumulated_grad_batches = (
              self.meta_batch_size / trainer.world_size
          )
      else:
          devices = 1
          is_data_parallel = isinstance(
              trainer.strategy, DataParallelStrategy
          )
          if is_data_parallel:
              # when using DP, we need to sample n tasks, so it can split across multiple devices.
              devices = accelerator_connector(trainer).devices
          dataset = TaskDataParallel(
              taskset,
              epoch_length=epoch_length,
              devices=devices,
              collate_fn=None,
          )
          self.trainer.accumulated_grad_batches = (
              self.meta_batch_size / devices
          )
  
      return dataset
  
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 5 for calling the Learn2LearnAdapter obj's underlying member method code with example usage: obj._convert_dataset(trainer, dataset, ways, shots, queries, num_workers, num_task, epoch_length) and returns: dataset