docstr: Defines the transform to be applied on a single sample on device for the testing stage.
  
  The input data of the transform would have the following form::
  
      {
          DataKeys.INPUT: ...,
          DataKeys.TARGET: ...,
          DataKeys.METADATA: ...,
      }
docstr_tok: ["Defines","the","transform","to","be","applied","on","a","single","sample","on","device","for","the","testing","stage",".","The","input","data","of","the","transform","would","have","the","following","form",":",":","{","DataKeys.INPUT",":","...",",","DataKeys.TARGET",":","...",",","DataKeys.METADATA",":","...",",","}"]
code: def test_per_sample_transform_on_device(self) -> Callable:
      """Defines the transform to be applied on a single sample on device for the testing stage.
  
      The input data of the transform would have the following form::
  
          {
              DataKeys.INPUT: ...,
              DataKeys.TARGET: ...,
              DataKeys.METADATA: ...,
          }
  
      """
      return self.per_sample_transform_on_device()
  
imports: import inspect
  from dataclasses import dataclass
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
  from pytorch_lightning.utilities import rank_zero_warn
  from pytorch_lightning.utilities.enums import LightningEnum
  from flash.core.data.callback import ControlFlow
  from flash.core.data.utilities.collate import default_collate
  from flash.core.data.utils import _STAGES_PREFIX
  from flash.core.utilities.stages import RunningStage
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
label_desc: Use image node_id 12 for calling the InputTransform obj's underlying member method code with example usage: obj.test_per_sample_transform_on_device() and returns: self