code: def merge_state_dict(
      self,
      mp_world_size,
      mp_rank,
      quantize=False,
      quantize_bits=8,
      groups=64,
      mlp_extra_grouping=True,
  ):
      self.sanity_check(self.ckpt_list[0])
  
      sd_list = self.get_merge_state_dicts(mp_world_size, mp_rank)
      ds_sd = copy.deepcopy(sd_list[0])
      new_client_sd = collections.OrderedDict()
  
      client_sd_list = [self.get_module(sd) for sd in sd_list]
      keys = client_sd_list[0].keys()
  
      ckpt_ver = self.get_checkpoint_version(ds_sd)
      logger.info(f"checkpoint version: {ckpt_ver}")
      if quantize:
          quantizer = WeightQuantization(
              mlp_extra_grouping=mlp_extra_grouping,
              mp_size=mp_world_size,
          )
  
      for key in keys:
          value_list = [sd[key] for sd in client_sd_list]
  
          if (
              "attention.dense.weight" in key
              or "mlp.dense_4h_to_h.weight" in key
          ):
              if quantize:
                  value_list = quantizer.Quantize(
                      value_list,
                      quantize_bits,
                      groups,
                      key=key,
                      merge_dim=1,
                  )
              new_client_sd[key] = torch.cat(value_list, axis=1)
          elif "attention.query_key_value" in key:
              if quantize and "attention.query_key_value.weight" in key:
                  value_list = quantizer.Quantize(
                      value_list, quantize_bits, groups, key=key
                  )
                  new_client_sd[key] = torch.cat(value_list, axis=0)
              else:
                  if quantize:
                      new_client_sd[key] = torch.cat(value_list, axis=0)
                  else:
                      new_client_sd[key] = self.merge_query_key_value(
                          value_list, ckpt_ver
                      )
          elif (
              "mlp.dense_h_to_4h.weight" in key
              or "word_embeddings.weight" in key
              or "mlp.dense_h_to_4h.bias" in key
          ):
              if quantize and "mlp.dense_h_to_4h.weight" in key:
                  value_list = quantizer.Quantize(
                      value_list, quantize_bits, groups, key=key
                  )
              new_client_sd[key] = torch.cat(value_list, axis=0)
          else:
              new_client_sd[key] = value_list[0]
      if quantize:
          all_scales = quantizer.merge_scales()
      ds_sd = self.set_module(ds_sd, new_client_sd)
  
      return (
          ds_sd,
          (all_scales if quantize else None),
          len(client_sd_list),
      )
  
imports: import torch
  import os
  import copy
  import collections
  import json
  from abc import ABC, abstractmethod
  from deepspeed.utils import logger
  from deepspeed.runtime.checkpoint_engine.torch_checkpoint_engine import TorchCheckpointEngine
  from .weight_quantizer import WeightQuantization
label_desc: Use image node_id 4 for calling the MegatronSDLoader obj's underlying member method code with example usage: obj.merge_state_dict(mp_world_size, mp_rank, quantize, quantize_bits, groups, mlp_extra_grouping) and returns: ds_sd,  unknown,  len