code: def _generate_tensor_methods_for_privateuse1_backend(
      custom_backend_name: str,
  ) -> None:
      @property  # type: ignore[misc]
      def wrap_tensor_backend(self: torch.Tensor) -> bool:
          return self.device.type == custom_backend_name
  
      _check_register_once(torch.Tensor, f"is_{custom_backend_name}")
      setattr(
          torch.Tensor, f"is_{custom_backend_name}", wrap_tensor_backend
      )
  
      def wrap_tensor_to(
          self: torch.Tensor,
          device: Optional[Union[int, torch.device]] = None,
          non_blocking=False,
          **kwargs,
      ) -> torch.Tensor:
          r"""Perform Tensor device conversion. Call the to operator implementation.
  
          .. note::
              If the ``self`` Tensor already
              has the correct :class:`torch.device`, then ``self`` is returned.
              Otherwise, the returned tensor is a copy of ``self`` with the desired :class:`torch.device`.
  
          Args:
              device (int, optional): if specified, all parameters will be copied to that device
              non_blocking (bool): If ``True`` and the source is in pinned memory,
                  the copy will be asynchronous with respect to the host. Otherwise,
                  the argument has no effect.
              **kwargs (dict): For compatibility, may contain the key ``memory_format`` argument.
          """
          device_idx = _normalization_device(
              custom_backend_name, device
          )
          return self.to(
              device=torch.device(
                  f"{custom_backend_name}:{device_idx}"
              ),
              non_blocking=non_blocking,
              **kwargs,
          )
  
      _check_register_once(torch.Tensor, custom_backend_name)
      setattr(torch.Tensor, custom_backend_name, wrap_tensor_to)
  
file_path: pytorch/torch/utils/backend_registration.py
imports: import torch
  from torch._C import _rename_privateuse1_backend, _get_privateuse1_backend_name
  from typing import List, Optional, Union
label_desc: Use image node_id 4 for calling a global function with example usage: _generate_tensor_methods_for_privateuse1_backend(custom_backend_name) and returns: unknown, self