class_docstr: Run training in mixed precision
class_docstr_tok: ["Run","training","in","mixed","precision"]
code: def after_pred(self):
      self.learn.pred = to_float(self.pred)
  
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 4 for calling the NonNativeMixedPrecision obj's underlying member method code with example usage: obj.after_pred() without return types