class_docstr: This is a simple abstraction for FSDP computing devices,
  which enables custom backends that implement CUDA-like
  semantics to be integrated with FSDP.
class_docstr_tok: ["This","is","a","simple","abstraction","for","FSDP","computing","devices",",","which","enables","custom","backends","that","implement","CUDA-like","semantics","to","be","integrated","with","FSDP","."]
code: def __getattr__(self, __name: str) -> Any:
      try:
          return getattr(self.__backend, __name)
      except AttributeError as exc:
          raise AttributeError(
              f"Custom backend '{self.__device.type}' not implement 'torch.{self.__device.type}.{__name}'"
          ) from exc
  
imports: import logging
  import traceback
  import warnings
  import weakref
  from enum import auto, Enum
  from functools import partial
  from typing import Any, Callable, cast, Dict, Generator, Iterable, List, no_type_check, Optional, Set, Tuple, Type, TYPE_CHECKING
  import torch
  import torch.distributed
  import torch.distributed.fsdp._flat_param
  import torch.nn
  from torch.distributed._composable_state import _get_module_state, _State
  from torch.distributed._tensor.device_mesh import DeviceMesh
  from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import _CHECKPOINT_PREFIX
  from torch.distributed.fsdp._fsdp_extensions import FSDPExtensions
  from torch.distributed.utils import _apply_to_tensors
  from torch.utils._mode_utils import no_dispatch
  from .api import FullOptimStateDictConfig, FullStateDictConfig, OptimStateDictConfig, ShardingStrategy, StateDictConfig, StateDictType
label_desc: Use image node_id 3 for calling the _FSDPDeviceHandle obj's underlying member method code with example usage: obj.__getattr__(__name) and returns: getattr