code: def __new__(cls, *args, **kwargs):
      t, kwargs = cls.get_wrapper_properties(*args, **kwargs)
      if "size" not in kwargs:
          size = t.size()
      else:
          size = kwargs["size"]
          del kwargs["size"]
      if "dtype" not in kwargs:
          kwargs["dtype"] = t.dtype
      if "layout" not in kwargs:
          kwargs["layout"] = t.layout
      if "device" not in kwargs:
          kwargs["device"] = t.device
      if "requires_grad" not in kwargs:
          kwargs["requires_grad"] = False
      # Ignore memory_format and pin memory for now as I don't know how to
      # safely access them on a Tensor (if possible??)
  
      wrapper = torch.Tensor._make_wrapper_subclass(cls, size, **kwargs)
      wrapper._validate_methods()
      return wrapper
  
imports: import torch
  from copy import deepcopy
  from torch.utils._pytree import tree_map
  from torch.testing._internal.logging_tensor import LoggingTensor
label_desc: Use image node_id 1 for calling the WrapperTensor obj's underlying member method code with example usage: obj.__new__(cls) and returns: wrapper