code: def make_functional_with_buffers_deprecated_v1(model: nn.Module):
      """make_functional_with_buffers_deprecated_v1(model) -> weights, buffers, func, weight_names, buffer_names
  
      Given an nn.Module, make_functional_with_buffers_deprecated_v1 extracts the state (weights and buffers)
      and returns a functional version of the model, `func`.
  
      `func` can be invoked as follows:
      ```
      x = torch.randn(4, 3)
      model = nn.Linear(3, 3)
      weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model)
      func(weights, buffers, (x,))
      ```
  
      And here is an example of applying the grad transform:
      ```
      x = torch.randn(4, 3)
      model = nn.Linear(3, 3)
      weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model)
      func(weights, buffers, (x,))
      grad_weights = grad(func)(weights, buffers, (x,))
      ```
  
      To put the state back into a model, use `load_state`.
      """
      weights, weight_descriptors, _ = extract_weights(model)
      buffers, buf_descriptors, _ = extract_buffers(model)
  
      def fun(weights, buffers, data):
          mutable_model = copy.deepcopy(model)
          load_weights(mutable_model, weight_descriptors, weights)
          load_buffers(mutable_model, buf_descriptors, buffers)
          return mutable_model(*data)
  
      return weights, buffers, fun, weight_descriptors, buf_descriptors
  
imports: import copy
  from typing import Any, Callable, Dict, Iterable, List, NoReturn, Sequence, Tuple, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn.utils._named_member_accessor import NamedMemberAccessor
label_desc: Use image node_id 11 for calling a global function with example usage: make_functional_with_buffers_deprecated_v1(model) and returns: weights,  buffers,  fun,  weight_descriptors,  buf_descriptors, mutable_model