class_docstr: This wraps `default_prepare_batch` to return `image` and `label` only, so is consistent with its API.
class_docstr_tok: ["This","wraps","`","default_prepare_batch","`","to","return","`","image","`","and","`","label","`","only",",","so","is","consistent","with","its","API","."]
docstr: 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.
docstr_tok: ["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","."]
code: def __call__(
      self,
      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:
      """
      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.
  
      """
      return default_prepare_batch(
          batchdata, device, non_blocking, **kwargs
      )
  
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 PrepareBatchDefault obj's underlying member method code with example usage: obj.__call__(batchdata, device, non_blocking) and returns: default_prepare_batch