code: def get_data():
      ds = load_dataset("beans")
  
      X_train = ds["train"]["image"]
      y_train = np.array(ds["train"]["labels"])
  
      X_valid = ds["validation"]["image"]
      y_valid = np.array(ds["validation"]["labels"])
  
      return X_train, X_valid, y_train, y_valid
  
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 1 for calling a global function with example usage: get_data() and returns: X_train,  X_valid,  y_train,  y_valid