code: def _generate_storage_methods_for_privateuse1_backend(
      custom_backend_name: str,
      unsupported_dtype: Optional[List[torch.dtype]] = None,
  ) -> None:
      # Attribute is registered in the _StorageBase class
      # and UntypedStorage obtains through inheritance.
      @property  # type: ignore[misc]
      def wrap_storage_backend(
          self: torch.storage._StorageBase,
      ) -> bool:
          r"""Return the internal :class:`torch.UntypedStorage`."""
          return self.device.type == custom_backend_name
  
      _check_register_once(
          torch.storage._StorageBase, f"is_{custom_backend_name}"
      )
      setattr(
          torch.storage._StorageBase,
          f"is_{custom_backend_name}",
          wrap_storage_backend,
      )
  
      def wrap_storage_to(self, device=None, non_blocking=False):
          r"""Return a copy of this object in custom device memory.
  
          If this object is already in device memory and on the correct device, then
          no copy is performed and the original object is returned.
  
          Args:
              device (int): The destination device id. Defaults to the current 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.
          """
          # There should be a judgment related to storage device and a judgment related to storage type,
          # but it depends on the extended function, so this part is temporarily omitted in the automatic generation.
          device_idx = _normalization_device(
              custom_backend_name, device
          )
  
          if getattr(self, f"is_{custom_backend_name}"):
              # storage has already on expected device.
              if self.get_device() == device_idx:
                  return self
          # For sparse storage, custom need to extend the implementation by themselves.
          if self.is_sparse:
              raise RuntimeError(
                  f"Can not support a sparse storage move to {custom_backend_name} backend"
              )
          # create untyped_storage and copy data
          untyped_storage = torch.UntypedStorage(
              self.size(),
              device=torch.device(
                  f"{custom_backend_name}:{device_idx}"
              ),
          )
          untyped_storage.copy_(self, non_blocking)
          return untyped_storage
  
      _check_register_once(
          torch.storage._StorageBase, custom_backend_name
      )
      setattr(
          torch.storage._StorageBase,
          custom_backend_name,
          wrap_storage_to,
      )
  
      # Register the corresponding attribute for the TypedStorage class.
      # When the TypedStorage class is removed, the registration is also removed.
  
      @property  # type: ignore[misc]
      def wrap_typed_storage_backend(
          self: torch.storage.TypedStorage,
      ) -> bool:
          torch.storage._warn_typed_storage_removal()
          return (
              self._untyped_storage.device.type == custom_backend_name
          )
  
      _check_register_once(
          torch.TypedStorage, f"is_{custom_backend_name}"
      )
      setattr(
          torch.storage.TypedStorage,
          f"is_{custom_backend_name}",
          wrap_typed_storage_backend,
      )
  
      def wrap_typed_storage_to(
          self: torch.storage.TypedStorage,
          device=None,
          non_blocking=False,
          **kwargs,
      ) -> torch.storage.TypedStorage:
          torch.storage._warn_typed_storage_removal()
          if unsupported_dtype and self.dtype in unsupported_dtype:
              raise RuntimeError(
                  f"Cannot create {custom_backend_name} storage "
                  f"as {self.dtype} dtype is not supported by this backend"
              )
          custom_backend_storage: torch.UntypedStorage = getattr(
              self._untyped_storage, custom_backend_name
          )(device, non_blocking, **kwargs)
          return self._new_wrapped_storage(custom_backend_storage)
  
      _check_register_once(torch.TypedStorage, custom_backend_name)
      setattr(
          torch.TypedStorage, custom_backend_name, wrap_typed_storage_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 6 for calling a global function with example usage: _generate_storage_methods_for_privateuse1_backend(custom_backend_name, unsupported_dtype) and returns: unknown, untyped_storage, unknown, self, self