class_docstr: Interface of customized prepare_batch in the trainer or evaluator workflows.
  It takes the data of current batch, target device and non_blocking flag as input.
  Args `batchdata`, `device`, `non_blocking` refer to the ignite API:
  https://pytorch.org/ignite/v0.4.8/generated/ignite.engine.create_supervised_trainer.html.
  `kwargs` supports other args for `Tensor.to()` API.
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code: def __call__(
      self,
      batchdata: dict[str, torch.Tensor],
      device: str | torch.device | None = None,
      non_blocking: bool = False,
      **kwargs: Any,
  ) -> Any:
      raise NotImplementedError(
          f"Subclass {self.__class__.__name__} must implement this method."
      )
  
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 1 for calling the PrepareBatch obj's underlying member method code with example usage: obj.__call__(batchdata, device, non_blocking) without return types