code: def copy(self, dst, src, int8=False, allocate_tensor=False):
      if src is None:
          return src
      assert (
          not dst.data.is_meta
      )  # the torch.Tensor.copy_ method used below will silently fail on meta tensors
      if allocate_tensor:
          dst = torch.empty_like(dst)
      outer_dim = 0 if int8 else 1
      inner_dim = 1 if int8 else 0
      src_shape = src.shape
      dst_shape = dst.shape
      if len(src_shape) == 2 and len(dst_shape) == 2:
          if (
              src_shape[inner_dim] == dst_shape[self.in_dim]
              and src_shape[outer_dim] == dst_shape[self.out_dim]
          ):
              dst = (
                  dst.reshape(-1)
                  .data.copy_(src.data.reshape(-1))
                  .reshape(src.shape)
              )
          else:
              if src_shape[inner_dim] != dst_shape[self.in_dim]:
                  self.merge_assert(
                      src_shape[inner_dim], dst_shape[self.in_dim]
                  )
                  dst.data.copy_(
                      src[
                          :,
                          self.gpu_index
                          * dst_shape[self.in_dim] : (
                              self.gpu_index + 1
                          )
                          * dst_shape[self.in_dim],
                      ]
                      if inner_dim == 1
                      else src[
                          self.gpu_index
                          * dst_shape[self.in_dim] : (
                              self.gpu_index + 1
                          )
                          * dst_shape[self.in_dim],
                          :,
                      ]
                  )
              else:
                  self.merge_assert(
                      src_shape[outer_dim], dst_shape[self.out_dim]
                  )
                  dst.data.copy_(
                      src[
                          :,
                          self.gpu_index
                          * dst_shape[self.out_dim] : (
                              self.gpu_index + 1
                          )
                          * dst_shape[self.out_dim],
                      ]
                      if outer_dim == 1
                      else src[
                          self.gpu_index
                          * dst_shape[self.out_dim] : (
                              self.gpu_index + 1
                          )
                          * dst_shape[self.out_dim],
                          :,
                      ]
                  )
      else:
          if src_shape[0] == dst_shape[0]:
              dst = (
                  src if src.dtype == dst.dtype else dst.data.copy_(src)
              )
          else:
              dst.data.copy_(
                  src[
                      self.gpu_index
                      * dst_shape[-1] : (self.gpu_index + 1)
                      * dst_shape[-1]
                  ]
              )
      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 4 for calling the ReplaceWithTensorSlicing obj's underlying member method code with example usage: obj.copy(dst, src, int8, allocate_tensor) and returns: dst, src