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: The main params of the model.
  
  Returns the plain model params here. Maybe different in other precision plugins.
docstr_tok: ["The","main","params","of","the","model",".","Returns","the","plain","model","params","here",".","Maybe","different","in","other","precision","plugins","."]
code: def main_params(self, optimizer: Optimizer) -> _PARAMETERS:
      """The main params of the model.
  
      Returns the plain model params here. Maybe different in other precision plugins.
  
      """
      for group in optimizer.param_groups:
          yield from group["params"]
  
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 11 for calling the Precision obj's underlying member method code with example usage: obj.main_params(optimizer) without return types