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: Instantiate module parameters or tensors in the precision type this plugin handles.
  
  This is optional and depends on the precision limitations during optimization.
docstr_tok: ["Instantiate","module","parameters","or","tensors","in","the","precision","type","this","plugin","handles",".","This","is","optional","and","depends","on","the","precision","limitations","during","optimization","."]
code: def module_init_context(self) -> ContextManager:
      """Instantiate module parameters or tensors in the precision type this plugin handles.
  
      This is optional and depends on the precision limitations during optimization.
  
      """
      return nullcontext()
  
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 3 for calling the Precision obj's underlying member method code with example usage: obj.module_init_context() and returns: nullcontext