class_docstr: Base class for all plugins handling the precision-specific parts of the training.
  
  The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.
class_docstr_tok: ["Base","class","for","all","plugins","handling","the","precision-specific","parts","of","the","training",".","The","class","attribute","precision","must","be","overwritten","in","child","classes",".","The","default","value","reflects","fp32","training","."]
docstr: A contextmanager for the test step.
docstr_tok: ["A","contextmanager","for","the","test","step","."]
code: def test_step_context(self) -> Generator[None, None, None]:
      """A contextmanager for the test step."""
      with self.forward_context():
          yield
  
imports: import contextlib
  from functools import partial
  from typing import Any, Callable, Generator, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins import Precision
  from lightning.fabric.utilities.types import Steppable
  from lightning.pytorch.core.hooks import CheckpointHooks
  from lightning.pytorch.trainer import call
  from lightning.pytorch.utilities import GradClipAlgorithmType
label_desc: Use image node_id 14 for calling the Precision obj's underlying member method code with example usage: obj.test_step_context() without return types