code: def load_buffer(module, state_dict, prefix):
      for name in module._buffers.keys():
          if module._buffers[name].data.is_meta:
              module._buffers[name] = torch.nn.parameter.Parameter(
                  data=torch.empty_like(
                      module._buffers[name].data, device="cpu"
                  ),
                  requires_grad=module._buffers[
                      name
                  ].data.requires_grad,
              )
          if prefix + name in state_dict.keys():
              module._buffers[name].data.copy_(
                  state_dict[prefix + name]
              )
  
imports: import re
  from torch import nn
  from .replace_policy import replace_policies
  from typing import Optional
  import torch
  from deepspeed import comm
  from .layers import LinearAllreduce, LinearLayer, LmHeadLinearAllreduce
  from deepspeed.accelerator import get_accelerator
  from .fusedqkv_utils import require_tp_fused_qkvw, prepare_tp_fused_qkvw
  from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
label_desc: Use image node_id 2 for calling the Loading obj's underlying member method code with example usage: obj.load_buffer(module, state_dict, prefix) without return types