class_docstr: Mixed precision training using Pytorch's Automatic Mixed Precision (AMP)
class_docstr_tok: ["Mixed","precision","training","using","Pytorch","'s","Automatic","Mixed","Precision","(","AMP",")"]
docstr: Pretend to be an optimizer for `GradScaler`
docstr_tok: ["Pretend","to","be","an","optimizer","for","`","GradScaler","`"]
code: def param_groups(self):
      "Pretend to be an optimizer for `GradScaler`"
      return self.opt.param_groups
  
imports: from __future__ import annotations
  from ..basics import *
  from .progress import *
  from torch.cuda.amp import GradScaler, autocast
  from torch.cuda.amp.grad_scaler import OptState
  from ..fp16_utils import convert_network, model_grads_to_master_grads, master_params_to_model_params
  from torch.nn.utils import parameters_to_vector
label_desc: Use image node_id 10 for calling the MixedPrecision obj's underlying member method code with example usage: obj.param_groups() and returns: self