code: def strided_copy(
      self,
      dst: Optional[torch.Tensor],
      src: Optional[torch.Tensor],
      num_splits: int,
      int8: bool = False,
      allocate_tensor: bool = False,
  ):
      if src is None:
          return src
      src_shape = src.shape
      dst_shape = dst.shape
  
      outer_dim = 0 if int8 else -1
  
      if allocate_tensor:
          dst = torch.empty_like(dst)
  
      src_split = torch.split(
          src.data, src.shape[outer_dim] // num_splits, dim=outer_dim
      )
      if len(src_shape) == 2 and len(dst_shape) == 2:
          if src_shape[outer_dim] == dst_shape[self.out_dim]:
              try:
                  dst = (
                      dst.reshape(-1)
                      .data.copy_(src.data.reshape(-1))
                      .reshape(src.shape)
                  )
              except:
                  print(dst.shape, src.shape)
                  exit()
              dst = torch.nn.parameter.Parameter(
                  dst, requires_grad=False
              )
              if hasattr(src, "scale"):
                  dst.scale = src.scale
              return dst
          self.merge_assert(
              src_shape[outer_dim], dst_shape[self.out_dim]
          )
          qkv_size = dst_shape[self.out_dim] // num_splits
          qkv_split = [
              torch.split(src_s, qkv_size, dim=outer_dim)
              for src_s in src_split
          ]
          weight_split = [
              torch.cat(
                  [qkv_s[i] for qkv_s in qkv_split], axis=outer_dim
              )
              for i in range(len(qkv_split[0]))
          ]
          dst = (
              dst.reshape(-1)
              .data.copy_(
                  weight_split[self.gpu_index].contiguous().reshape(-1)
              )
              .reshape(weight_split[self.gpu_index].shape)
          )
      else:
          if src_shape[0] == dst_shape[0]:
              return torch.nn.parameter.Parameter(src)
          qkv_size = dst_shape[0] // num_splits
          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].contiguous())
  
      dst = torch.nn.parameter.Parameter(dst, requires_grad=False)
      if hasattr(src, "scale"):
          dst.scale = src.scale
      return dst
  
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 3 for calling the ReplaceWithTensorSlicing obj's underlying member method code with example usage: obj.strided_copy(dst, src, num_splits, int8, allocate_tensor) and returns: dst, src, dst, torch