code: def copy(self, dst, src):
      if src is None:
          return src
      src_shape = src.shape
      dst_shape = dst.shape
      if len(src_shape) == 2 and len(dst_shape) == 2:
          if (
              src_shape[0] == dst_shape[0]
              and src_shape[1] == dst_shape[1]
          ):
              dst.data.copy_(src)
          else:
              if src_shape[self.in_dim] != dst_shape[self.in_dim]:
                  self.merge_assert(
                      src_shape[self.in_dim], dst_shape[self.in_dim]
                  )
                  weight_split = (
                      torch.split(
                          src, dst_shape[self.in_dim], dim=self.in_dim
                      )[self.gpu_index]
                      .to(torch.cuda.current_device())
                      .contiguous()
                  )
              else:
                  self.merge_assert(
                      src_shape[self.out_dim], dst_shape[self.out_dim]
                  )
                  weight_split = (
                      torch.split(
                          src.data,
                          dst_shape[self.out_dim],
                          dim=self.out_dim,
                      )[self.gpu_index]
                      .to(torch.cuda.current_device())
                      .contiguous()
                  )
              dst.data.copy_(weight_split.contiguous())
      else:
          if src_shape[0] == dst_shape[0]:
              dst.data.copy_(src)
          else:
              bias_split = (
                  torch.split(src.data, dst_shape[-1])[self.gpu_index]
                  .to(torch.cuda.current_device())
                  .contiguous()
              )
              dst.data.copy_(bias_split)
      dst = torch.nn.parameter.Parameter(dst, requires_grad=False)
      if hasattr(src, "scale"):
          dst.scale = src.scale
      return 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 4 for calling the ReplaceWithTensorSlicing obj's underlying member method code with example usage: obj.copy(dst, src) and returns: dst, src