code: def model_times(self):
      assert (
          self.model_profile_enabled
      ), "model profiling is not enabled"
      model_times = self._model_times
      if self._config.enable_cuda_graph and len(self._model_times) == 0:
          raise ValueError(
              "Model times are empty and cuda graph is enabled. If "
              "this is a GPT-style model this combo is not supported. If this is a "
              "BERT-style model this is a bug, please report it. "
              f"Model type is: {type(self.module)}"
          )
      self._model_times = []
      return model_times
  
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 20 for calling the InferenceEngine obj's underlying member method code with example usage: obj.model_times() and returns: model_times