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.
docstr: Runs after precision plugin executes backward.
  
  Args:
      tensor: The tensor that will be used for backpropagation
      module: The module that was involved in producing the tensor and whose parameters need the gradients
docstr_tok: ["Runs","after","precision","plugin","executes","backward",".","Args",":","tensor",":","The","tensor","that","will","be","used","for","backpropagation","module",":","The","module","that","was","involved","in","producing","the","tensor","and","whose","parameters","need","the","gradients"]
code: def post_backward(
      self, tensor: Tensor, module: Optional[Module]
  ) -> Any:
      """Runs after precision plugin executes backward.
  
      Args:
          tensor: The tensor that will be used for backpropagation
          module: The module that was involved in producing the tensor and whose parameters need the gradients
  
      """
  
imports: from contextlib import nullcontext
  from typing import Any, ContextManager, Dict, Literal, Optional, Union
  from torch import Tensor
  from torch.nn import Module
  from torch.optim import Optimizer
  from lightning.fabric.utilities.types import _PARAMETERS, Optimizable
label_desc: Use image node_id 9 for calling the Precision obj's underlying member method code with example usage: obj.post_backward(tensor, module) without return types