code: def train(
      seed=1234,
      device="cuda",
      output_file=None,
      # max epochs need to be known beforehand for lr scheduler, so set it explicitly
      **kwargs,
  ):
      parsed = parse_args(kwargs, defaults=DEFAULTS)
      if kwargs.get("help"):
          # don't need to run expensive steps below
          parsed(get_model(num_classes=3, lr_lambda=None))
          return
  
      torch.manual_seed(seed)
      # set the same device for all pipeline steps
      kwargs["net__device"] = kwargs[
          "feature_extractor__device"
      ] = device
  
      X_train, X_valid, y_train, y_valid = get_data()
      num_classes = len(set(y_train))
      max_epochs = kwargs.get(
          "net__max_epochs", DEFAULTS["net__max_epochs"]
      )
      lr_lambda_schedule = partial(
          lr_lambda, num_warmup_steps=0.0, num_training_steps=max_epochs
      )
      pipe = parsed(
          get_model(
              num_classes=num_classes, lr_lambda=lr_lambda_schedule
          )
      )
  
      pipe.fit(X_train, y_train)
      y_pred = pipe.predict(X_valid)
      print(
          f"Accuracy on validation dataset is {accuracy_score(y_valid, y_pred):.3f}"
      )
  
      if output_file:
          save_model(pipe, output_file, trim=True)
  
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 5 for calling a global function with example usage: train(seed, device, output_file) without return types