class_docstr: A generic `ModelHook` that ensures inputs and model weights are on the same device for the forward pass of the
  associated module, potentially offloading the weights after the forward pass.
  
  Args:
      execution_device (`torch.device`, *optional*):
          The device on which inputs and model weights should be placed before the forward pass.
      offload (`bool`, *optional*, defaults to `False`):
          Whether or not the weights should be offloaded after the forward pass.
      io_same_device (`bool`, *optional*, defaults to `False`):
          Whether or not the output should be placed on the same device as the input was.
      weights_map (`Mapping[str, torch.Tensor]`, *optional*):
          When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values.
      offload_buffers (`bool`, *optional*, defaults to `False`):
          Whether or not to include the associated module's buffers when offloading.
      place_submodules (`bool`, *optional*, defaults to `False`):
          Whether to place the submodules on `execution_device` during the `init_hook` event.
code: def __repr__(self):
      return (
          f"AlignDevicesHook(execution_device={self.execution_device}, offload={self.offload}, "
          f"io_same_device={self.io_same_device}, offload_buffers={self.offload_buffers}, "
          f"place_submodules={self.place_submodules}, skip_keys={repr(self.skip_keys)})"
      )
  
imports: import functools
  from typing import Dict, List, Mapping, Optional, Union
  import torch
  import torch.nn
  from .state import PartialState
  from .utils import PrefixedDataset, find_device, named_module_tensors, send_to_device, set_module_tensor_to_device
  from .utils.modeling import get_non_persistent_buffers
label_desc: Use image node_id 2 for calling the AlignDevicesHook obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self, str, self, str, repr, str