code: def _convert_cls(
      tensor: "LazyTensor", target: torch.Tensor
  ) -> torch.Tensor:
      """Convert a lazy tensor's class to target's class, with target's data.
  
      The reason why we change the class of a lazy tensor in-place is that this can easily handle shared modules/parameters, which is common in huggingface models.
      If we create a new tensor and update the module by ``setattr(module, name, param)``, the shared parameters will not be updated. And we have to track all shared parameters and update them manually.
  
      Args:
          tensor (LazyTensor): the LazyTensor to be converted
          target (torch.Tensor): target tensor
  
      Returns:
          torch.Tensor: the converted tensor
      """
      cls_to_become = (
          Parameter if isinstance(tensor, Parameter) else torch.Tensor
      )
      tensor.__class__ = cls_to_become
      if cls_to_become is Parameter:
          # to fit UninitializedParameter
          delattr(tensor, "_is_param")
      tensor.data = target
      tensor.requires_grad = target.requires_grad
      # subclass of torch.Tensor does not have tolist() method
      # overwrite this method after materialization or distribution
      tensor.tolist = MethodType(_data_tolist, tensor)
      return tensor
  
imports: from types import MethodType
  from typing import Callable, Optional, Union
  import torch
  import torch.nn
  from packaging import version
  from torch import Tensor
  from torch.nn import Parameter
  from torch.utils._pytree import tree_map
  from colossalai.logging import get_dist_logger
  from .construction import ConstructorManager
  from .pretrained import PretrainedManager
  import colossalai._analyzer._subclasses._meta_registration
label_desc: Use image node_id 2 for calling a global function with example usage: _convert_cls(tensor, target) and returns: tensor