code: def _graph_replay(self, *inputs, **kwargs):
      for i in range(len(inputs)):
          if torch.is_tensor(inputs[i]):
              self.static_inputs[i].copy_(inputs[i])
      for k in kwargs:
          if torch.is_tensor(kwargs[k]):
              self.static_kwargs[k].copy_(kwargs[k])
      self._cuda_graphs.replay()
      return self.static_output
  
imports: import torch
  import time
  import os
  from deepspeed import comm
  from deepspeed.utils.logging import log_dist
  from torch.nn.modules import Module
  from packaging import version
  from deepspeed.runtime.checkpoint_engine.torch_checkpoint_engine import TorchCheckpointEngine
  from deepspeed.utils.timer import SynchronizedWallClockTimer
  from ..runtime.state_dict_factory import SDLoaderFactory
  from ..runtime.weight_quantizer import WeightQuantization
  from ..module_inject.replace_module import replace_transformer_layer, generic_injection
  from ..comm.comm import init_distributed
  from ..pipe import PipelineModule
  from ..moe.utils import has_moe_layers
  from ..runtime.zero import GatheredParameters
  from ..module_inject import LinearAllreduce, LinearLayer, Normalize, ReplaceWithTensorSlicing
  from ..module_inject.replace_policy import DSPolicy
  from torch import nn
label_desc: Use image node_id 19 for calling the InferenceEngine obj's underlying member method code with example usage: obj._graph_replay() and returns: self