code: def save_model(pipe, output_file, trim=True):
      if trim:
          print(
              "Trimming net, cannot be trained further, only use for prediction"
          )
          pipe.steps[-1][1].trim_for_prediction()
  
      with open(output_file, "wb") as f:
          pickle.dump(pipe, f)
      print(f"Successfully saved model in {output_file}")
  
imports: from functools import partial
  import pickle
  import fire
  import numpy
  import torch
  from datasets import load_dataset
  from skorch.helper import parse_args
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.metrics import accuracy_score
  from sklearn.pipeline import Pipeline
  from skorch import NeuralNetClassifier
  from skorch.callbacks import ProgressBar, LRScheduler
  from torch import nn
  from torch.optim.lr_scheduler import LambdaLR
  from transformers import ViTFeatureExtractor, ViTForImageClassification
label_desc: Use image node_id 4 for calling a global function with example usage: save_model(pipe, output_file, trim) without return types