code: def _setup_for_real_optimizer(self):
      dp_world_size = dist.get_world_size(group=self.dp_process_group)
      self.partition_count = [
          dp_world_size for i in range(len(self.optimizer.param_groups))
      ]
  
      for i, param_group in enumerate(self.optimizer.param_groups):
          see_memory_usage(f"before initializing group {i}", force=True)
  
          partition_id = dist.get_rank(
              group=self.real_dp_process_group[i]
          )
  
          # grab the original list
          self.bf16_groups.append(param_group["params"])
  
          # create flat bf16 params
          self.bf16_groups_flat.append(
              self._flatten_dense_tensors_aligned(
                  self.bf16_groups[i],
                  self.nccl_start_alignment_factor * dp_world_size,
              )
          )
  
          # Make bf16 params point to flat tensor storage
          self._update_storage_to_flattened_tensor(
              tensor_list=self.bf16_groups[i],
              flat_tensor=self.bf16_groups_flat[i],
          )
  
          # divide flat weights into equal sized partitions
          partition_size = (
              self.bf16_groups_flat[i].numel() // dp_world_size
          )
          bf16_dp_partitions = [
              self.bf16_groups_flat[i].narrow(
                  0, dp_index * partition_size, partition_size
              )
              for dp_index in range(dp_world_size)
          ]
          self.bf16_partitioned_groups.append(bf16_dp_partitions)
  
          # create fp32 params partition
          self.fp32_groups_flat_partition.append(
              bf16_dp_partitions[partition_id].clone().float().detach()
          )
          self.fp32_groups_flat_partition[i].requires_grad = True
  
          num_elem_list = [t.numel() for t in self.bf16_groups[i]]
  
          # create fp32 gradients
          self.fp32_groups_gradients_flat.append(
              torch.zeros_like(
                  self.bf16_groups_flat[i], dtype=torch.float32
              )
          )
  
          # track individual fp32 gradients for entire model
          fp32_gradients = self._split_flat_tensor(
              flat_tensor=self.fp32_groups_gradients_flat[i],
              num_elem_list=num_elem_list,
          )
          self.fp32_groups_gradients.append(fp32_gradients)
  
          # flat tensor corresponding to actual fp32 gradients (i.e., minus alignment padding)
          length_without_padding = sum(num_elem_list)
          self.fp32_groups_actual_gradients_flat.append(
              torch.narrow(
                  self.fp32_groups_gradients_flat[i],
                  0,
                  0,
                  length_without_padding,
              )
          )
  
          # flat tensor corresponding to gradient partition
          self.fp32_groups_gradient_flat_partition.append(
              torch.narrow(
                  self.fp32_groups_gradients_flat[i],
                  0,
                  partition_id * partition_size,
                  partition_size,
              )
          )
  
          # track fp32 gradient updates
          self.fp32_groups_has_gradients.append(
              [False] * len(self.bf16_groups[i])
          )
  
          # Record padding required for alignment
          if (
              partition_id
              == dist.get_world_size(
                  group=self.real_dp_process_group[i]
              )
              - 1
          ):
              padding = (
                  self.bf16_groups_flat[i].numel()
                  - length_without_padding
              )
          else:
              padding = 0
  
          self.group_paddings.append(padding)
  
          # update optimizer param groups to reference fp32 params partition
          param_group["params"] = [self.fp32_groups_flat_partition[i]]
  
          see_memory_usage(f"after initializing group {i}", force=True)
  
      see_memory_usage("before initialize_optimizer", force=True)
      self.initialize_optimizer_states()
      see_memory_usage("end initialize_optimizer", force=True)
  
      # Need optimizer states initialized before linking lp to optimizer state
      self._link_all_hp_params()
      self._enable_universal_checkpoint()
      self._param_slice_mappings = self._create_param_mapping()
  
imports: from collections import OrderedDict
  import torch
  import sys
  import os
  from deepspeed import comm
  from deepspeed.runtime.constants import PIPE_REPLICATED
  from deepspeed.ops.op_builder import UtilsBuilder
  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
  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 2 for calling the BF16_Optimizer obj's underlying member method code with example usage: obj._setup_for_real_optimizer() without return types