class_docstr: Create Precision Plugin for IPEX BFloat16.
docstr: AMP for managing model forward/training_step/evaluation_step/predict_step.
docstr_tok: ["AMP","for","managing","model","forward\/training_step\/evaluation_step\/predict_step","."]
code: def forward_context(self):
      """AMP for managing model forward/training_step/evaluation_step/predict_step."""
      with torch.cpu.amp.autocast():
          yield
  
imports: import operator
  from contextlib import contextmanager
  from functools import partial
  from logging import warning
  from typing import Any, Union, Callable
  import torch
  from torch.nn import Module
  from torch.optim import Optimizer, LBFGS
  import pytorch_lightning
  from pytorch_lightning.strategies import SingleDeviceStrategy
  from pytorch_lightning.accelerators.accelerator import Accelerator
  from pytorch_lightning.plugins.precision import PrecisionPlugin
  from bigdl.nano.utils.common import compare_version
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.deps.ipex.ipex_api import ipex_optimize
label_desc: Use image node_id 1 for calling the IPEXBF16Precision obj's underlying member method code with example usage: obj.forward_context() without return types