code: def default_prepare_batch(
      batchdata: dict[str, torch.Tensor]
      | torch.Tensor
      | Sequence[torch.Tensor],
      device: str | torch.device | None = None,
      non_blocking: bool = False,
      **kwargs: Any,
  ) -> tuple[torch.Tensor, torch.Tensor | None] | torch.Tensor:
      """
      Default function to prepare the data for current iteration.
  
      The input `batchdata` is either a single tensor, a pair of tensors, or a dictionary of data. In the first case the
      return value is the tensor and None, in the second case the return value is the two tensors, and in the dictionary
      case the return value depends on what keys are present. if `CommonKeys.IMAGE` and `CommonKeys.LABEL` are present
      then the tensors they key to are returned, if only `CommonKeys.IMAGE` is present that tensor and None is returned.
      If `CommonKeys.REALS` is present this is returned with None. All returned tensors are moved to the given device
      using the given non-blocking argument before being returned.
  
      This function implements the expected API for a `prepare_batch` callable in Ignite:
      https://pytorch.org/ignite/v0.4.8/generated/ignite.engine.create_supervised_trainer.html
  
      Args:
          batchdata: input batch data which is either a single tensor, a pair, or a dictionary
          device: device to move every returned tensor to
          non_blocking: equivalent argument for `Tensor.to`
          kwargs: further arguments for `Tensor.to`
  
      Returns:
          image, label(optional).
      """
      if not isinstance(batchdata, dict):
          if isinstance(batchdata, torch.Tensor):
              return (
                  batchdata.to(
                      device=device, non_blocking=non_blocking, **kwargs
                  ),
                  None,
              )
          elif len(batchdata) == 2:
              image, label = batchdata
              return (
                  image.to(
                      device=device, non_blocking=non_blocking, **kwargs
                  ),
                  label.to(
                      device=device, non_blocking=non_blocking, **kwargs
                  ),
              )
  
          raise AssertionError(
              "Default prepare_batch expects a single tensor, a tensor pair, or dictionary input data."
          )
  
      if isinstance(batchdata.get(CommonKeys.LABEL), torch.Tensor):
          return (
              batchdata[CommonKeys.IMAGE].to(
                  device=device, non_blocking=non_blocking, **kwargs
              ),
              batchdata[CommonKeys.LABEL].to(
                  device=device, non_blocking=non_blocking, **kwargs
              ),
          )
  
      if GanKeys.REALS in batchdata:
          return batchdata[GanKeys.REALS].to(
              device=device, non_blocking=non_blocking, **kwargs
          )
  
      return (
          batchdata[CommonKeys.IMAGE].to(
              device=device, non_blocking=non_blocking, **kwargs
          ),
          None,
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Sequence
  from typing import TYPE_CHECKING, Any, cast
  import torch
  from monai.config import IgniteInfo
  from monai.transforms import apply_transform
  from monai.utils import ensure_tuple, min_version, optional_import
  from monai.utils.enums import CommonKeys, GanKeys
label_desc: Use image node_id 2 for calling a global function with example usage: default_prepare_batch(batchdata, device, non_blocking) and returns: batchdata,  None, batchdata,  batchdata, batchdata, batchdata,  None, image,  label