code: def _restore_from_bit16_weights(self):
      for i, group in enumerate(self.bf16_groups):
          partition_id = dist.get_rank(
              group=self.real_dp_process_group[i]
          )
          for bf16_partitions, fp32_partition in zip(
              self.bf16_partitioned_groups,
              self.fp32_groups_flat_partition,
          ):
              fp32_partition.data.copy_(
                  bf16_partitions[partition_id].data
              )
  
imports: from collections import OrderedDict
  import torch
  import sys
  import os
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from deepspeed import comm
  from deepspeed.runtime.constants import PIPE_REPLICATED
  from deepspeed.runtime import ZeROOptimizer
  from packaging import version
  from deepspeed.git_version_info import version
  from deepspeed.runtime.utils import get_global_norm_of_tensors, clip_tensors_by_global_norm, DummyOptim, align_dense_tensors, all_gather_dp_groups, bwc_tensor_model_parallel_rank, is_model_parallel_parameter, see_memory_usage, graph_process
  from deepspeed.utils import link_hp_params, fragment_address
  from deepspeed.checkpoint import enable_universal_checkpoint
  from deepspeed.checkpoint.constants import DS_VERSION, PARTITION_COUNT, BASE_OPTIMIZER_STATE, SINGLE_PARTITION_OF_FP32_GROUPS, CLIP_GRAD, GROUP_PADDINGS, PARAM_SLICE_MAPPINGS
label_desc: Use image node_id 19 for calling the BF16_Optimizer obj's underlying member method code with example usage: obj._restore_from_bit16_weights() without return types