code: def get_transformer_model():
      return Pipeline(
          [
              ("tokenizer", HuggingfacePretrainedTokenizer(TOKENIZER)),
              (
                  "net",
                  NeuralNetClassifier(
                      BertModule,
                      module__name=PRETRAINED_MODEL,
                      module__num_labels=2,
                      optimizer=OPTMIZER,
                      lr=LR,
                      max_epochs=MAX_EPOCHS,
                      criterion=CRITERION,
                      batch_size=BATCH_SIZE,
                      iterator_train__shuffle=True,
                      device=DEVICE,
                  ),
              ),
          ]
      )
  
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 2 for calling a global function with example usage: get_transformer_model() and returns: Pipeline