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: Fake optimizer step to detect whether this batch was skipped from `GradScaler`
docstr_tok: ["Fake","optimizer","step","to","detect","whether","this","batch","was","skipped","from","`","GradScaler","`"]
code: def step(self, *args, **kwargs):
      "Fake optimizer step to detect whether this batch was skipped from `GradScaler`"
      self.skipped = False
  
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 11 for calling the MixedPrecision obj's underlying member method code with example usage: obj.step() without return types