code: def get_model(num_classes, lr_lambda):
      pipe = Pipeline(
          [
              ("feature_extractor", FeatureExtractor()),
              (
                  "net",
                  NeuralNetClassifier(
                      VitModule,
                      callbacks=[
                          LRScheduler(LambdaLR, lr_lambda=lr_lambda),
                          ProgressBar(),
                      ],
                      module__num_classes=num_classes,
                  ),
              ),
          ]
      )
      return pipe
  
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 3 for calling a global function with example usage: get_model(num_classes, lr_lambda) and returns: pipe