class_docstr: DeepSpeed engine for training and inference.
class_docstr_tok: ["DeepSpeed","engine","for","training","and","inference","."]
code: def retake_inference_cache(self):
      if self._config.hybrid_engine.release_inference_cache:
          retake_success = inference_cuda_module.retake_workspace()
  
          if not retake_success:
              logger.warning(
                  "Unable to acquire workspace on first attempt, emptying cache and retrying."
              )
              gc.collect()
              get_accelerator().empty_cache()
              retake_success = inference_cuda_module.retake_workspace()
  
              if not retake_success:
                  raise RuntimeError(
                      "Unable to retake inference workspace."
                  )
  
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 10 for calling the DeepSpeedHybridEngine obj's underlying member method code with example usage: obj.retake_inference_cache() without return types