class_docstr: DeepSpeed engine for training and inference.
class_docstr_tok: ["DeepSpeed","engine","for","training","and","inference","."]
code: def step(self, lr_kwargs=None):
      super().step(lr_kwargs=lr_kwargs)
  
      if len(self._inference_containers) > 0:
          if not self.Z3_enabled:
              for inference_container in self._inference_containers:
                  inference_container.reset_params()
  
      if self._training_start_time is not None:
          self._training_latency += (
              time.time() - self._training_start_time
          )
          self._training_start_time = time.time()
  
imports: import torch
  from deepspeed.inference.config import DeepSpeedInferenceConfig
  from deepspeed.module_inject.replace_policy import replace_policies
  from deepspeed.module_inject.utils import policy_to_ds_container
  from .engine import DeepSpeedEngine
  from .utils import TLinear, get_inactive_params
  from deepspeed.runtime.zero import GatheredParameters
  import time
  import gc
  import math
  from deepspeed import comm
  from deepspeed.accelerator import get_accelerator
  from torch import nn
  from deepspeed.utils import logger
  from deepspeed.ops.op_builder import InferenceBuilder
  from deepspeed.module_inject.layers import LinearLayer, Normalize, EmbeddingLayer, OPTEmbedding
label_desc: Use image node_id 17 for calling the DeepSpeedHybridEngine obj's underlying member method code with example usage: obj.step(lr_kwargs) without return types