code: def load_20newsgroup_small():
      dataset = fetch_20newsgroups()
      y = dataset.target
      mask_0_or_1 = (y == 0) | (y == 1)
      X = np.asarray(dataset.data)[mask_0_or_1][:N_SAMPLES]
      y = dataset.target[mask_0_or_1][:N_SAMPLES]
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, stratify=y, random_state=0
      )
      return X_train, X_test, y_train, y_test
  
imports: import os
  import pickle
  from contextlib import contextmanager
  import numpy
  import torch
  from huggingface_hub import HfApi, create_repo, hf_hub_download
  from sklearn.datasets import fetch_20newsgroups, make_classification
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from torch import nn
  from transformers import AutoModelForSequenceClassification
  from skorch import NeuralNetClassifier
  from skorch.hf import HuggingfacePretrainedTokenizer
  from skorch.callbacks import TrainEndCheckpoint
  from skorch.hf import HfHubStorage
label_desc: Use image node_id 1 for calling a global function with example usage: load_20newsgroup_small() and returns: X_train,  X_test,  y_train,  y_test