code: def main(wrap_initial_model=True, wrap_loaded_model=True):
      X, y = make_classification(
          10000, n_features=100, n_informative=50, random_state=0
      )
      X = X.astype(np.float32)
  
      accelerator = Accelerator()
      model = get_accelerate_model(accelerator)
      model.unwrap_after_train = True if wrap_initial_model else False
      model.fit(X, y)
  
      model.save_params(f_params="model_params.pt")
      y_pred = model.predict(X)
      accuracy_before = accuracy_score(y, y_pred)
      print(f"Accuracy before loading: {accuracy_before}")
  
      if wrap_loaded_model:
          model_loaded = get_accelerate_model(accelerator).initialize()
      else:
          model_loaded = get_vanilla_model().initialize()
  
      model_loaded.load_params(f_params="model_params.pt")
      y_pred = model_loaded.predict(X)
      accuracy_after = accuracy_score(y, y_pred)
      print(f"Accuracy  after loading: {accuracy_after}")
  
      assert accuracy_before == accuracy_after
  
imports: import numpy
  import torch
  from accelerate import Accelerator
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from torch import nn
  from torch.distributed import TCPStore
  from skorch import NeuralNetClassifier
  from skorch.hf import AccelerateMixin
  from skorch.history import DistributedHistory
label_desc: Use image node_id 3 for calling a global function with example usage: main(wrap_initial_model, wrap_loaded_model) without return types