code: def qkv_copy(self, dst, src):
      if src is None:
          return src
      src_shape = src.shape
      dst_shape = dst.shape
  
      if self.out_dim == 0:
          src_split = torch.split(
              src.data, src_shape[self.out_dim] // self.mp_size, dim=0
          )
      else:
          src_split = torch.split(src.data, src.shape[-1] // 3, dim=-1)
      if len(src_shape) == 2 and len(dst_shape) == 2:
          if src_shape[self.out_dim] == dst_shape[self.out_dim]:
              return torch.nn.parameter.Parameter(src)
          if self.out_dim == 1:
              self.merge_assert(
                  src_shape[self.out_dim], dst_shape[self.out_dim]
              )
              qkv_size = dst_shape[self.out_dim] // 3
              qkv_split = [
                  torch.split(src_s, qkv_size, dim=self.out_dim)
                  for src_s in src_split
              ]
              weight_split = [
                  torch.cat(
                      [qkv_s[i] for qkv_s in qkv_split],
                      axis=self.out_dim,
                  )
                  for i in range(len(qkv_split[0]))
              ]
              dst.data.copy_(
                  weight_split[self.gpu_index]
                  .to(torch.cuda.current_device())
                  .contiguous()
              )
          else:
              dst.data.copy_(
                  src_split[self.gpu_index]
                  .to(torch.cuda.current_device())
                  .contiguous()
              )
      else:
          if src_shape[0] == dst_shape[0]:
              return torch.nn.parameter.Parameter(src)
          if self.out_dim == 1:
              qkv_size = dst_shape[0] // 3
              qkv_split = [
                  torch.split(src_s, qkv_size, dim=0)
                  for src_s in src_split
              ]
              bias_split = [
                  torch.cat([qkv_s[i] for qkv_s in qkv_split], axis=0)
                  for i in range(len(qkv_split[0]))
              ]
              dst.data.copy_(
                  bias_split[self.gpu_index]
                  .to(torch.cuda.current_device())
                  .contiguous()
              )
          else:
              dst.data.copy_(
                  src_split[self.gpu_index]
                  .to(torch.cuda.current_device())
                  .contiguous()
              )
  
      return torch.nn.parameter.Parameter(dst)
  
imports: import os
  import torch
  import tqdm
  import deepspeed
  import deepspeed.ops.transformer
  from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention
  from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock
  from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig
  from .replace_policy import HFBertLayerPolicy, HFGPT2LayerPolicy, BLOOMLayerPolicy
  from .replace_policy import replace_policies, generic_policies
  from deepspeed import comm
  from torch import nn
  from ..runtime.zero import GatheredParameters
  from .layers import LinearAllreduce, LinearLayer
  from .load_checkpoint import load_model_with_checkpoint
  import time
  from ..pipe import PipelineModule
label_desc: Use image node_id 3 for calling the ReplaceWithTensorSlicing obj's underlying member method code with example usage: obj.qkv_copy(dst, src) and returns: torch, src, torch, torch