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 init_hook(self, module):
      if not self.offload and self.execution_device is not None:
          for name, _ in named_module_tensors(
              module, recurse=self.place_submodules
          ):
              set_module_tensor_to_device(
                  module, name, self.execution_device
              )
      elif self.offload:
          self.original_devices = {
              name: param.device
              for name, param in named_module_tensors(
                  module, recurse=self.place_submodules
              )
          }
          if self.weights_map is None:
              self.weights_map = {
                  name: param.to("cpu")
                  for name, param in named_module_tensors(
                      module,
                      include_buffers=self.offload_buffers,
                      recurse=self.place_submodules,
                  )
              }
          for name, _ in named_module_tensors(
              module,
              include_buffers=self.offload_buffers,
              recurse=self.place_submodules,
              remove_non_persistent=True,
          ):
              set_module_tensor_to_device(module, name, "meta")
          if (
              not self.offload_buffers
              and self.execution_device is not None
          ):
              for name, _ in module.named_buffers(
                  recurse=self.place_submodules
              ):
                  set_module_tensor_to_device(
                      module, name, self.execution_device
                  )
          elif (
              self.offload_buffers and self.execution_device is not None
          ):
              for name in get_non_persistent_buffers(
                  module, recurse=self.place_submodules
              ):
                  set_module_tensor_to_device(
                      module, name, self.execution_device
                  )
  
      return module
  
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 3 for calling the AlignDevicesHook obj's underlying member method code with example usage: obj.init_hook(module) and returns: module