code: def test_tokenizers_transfomers():
      print("Testing tokenizers and transfomers started")
      torch.manual_seed(0)
      np.random.seed(0)
      X_train, X_test, y_train, y_test = load_20newsgroup_small()
  
      pipeline = get_transformer_model()
      pipeline.fit(X_train, y_train)
  
      with torch.inference_mode():
          y_pred = pipeline.predict(X_test)
  
      assert accuracy_score(y_test, y_pred) > 0.7
      print("Testing tokenizers and transfomers completed")
  
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 3 for calling a global function with example usage: test_tokenizers_transfomers() without return types