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",")"]
code: def before_backward(self):
      self.learn.loss_grad = self.scaler.scale(self.loss_grad)
  
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 6 for calling the MixedPrecision obj's underlying member method code with example usage: obj.before_backward() without return types