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: Performs the actual backpropagation.
  
  Args:
      tensor: The tensor that will be used for backpropagation
      model: The module that was involved in producing the tensor and whose parameters need the gradients
docstr_tok: ["Performs","the","actual","backpropagation",".","Args",":","tensor",":","The","tensor","that","will","be","used","for","backpropagation","model",":","The","module","that","was","involved","in","producing","the","tensor","and","whose","parameters","need","the","gradients"]
code: def backward(
      self,
      tensor: Tensor,
      model: Optional[Module],
      *args: Any,
      **kwargs: Any,
  ) -> None:
      """Performs the actual backpropagation.
  
      Args:
          tensor: The tensor that will be used for backpropagation
          model: The module that was involved in producing the tensor and whose parameters need the gradients
  
      """
      tensor.backward(*args, **kwargs)
  
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 8 for calling the Precision obj's underlying member method code with example usage: obj.backward(tensor, model) without return types