code: def performance_skorch(
      X_train,
      X_test,
      y_train,
      y_test,
      batch_size,
      device,
      lr,
      max_epochs,
  ):
      torch.manual_seed(0)
      net = NeuralNetClassifier(
          ClassifierModule,
          batch_size=batch_size,
          optimizer=torch.optim.Adadelta,
          lr=lr,
          device=device,
          max_epochs=max_epochs,
          callbacks=[
              (
                  "tr_acc",
                  EpochScoring(
                      "accuracy",
                      lower_is_better=False,
                      on_train=True,
                      name="train_acc",
                  ),
              ),
          ],
      )
      net.fit(X_train, y_train)
      y_pred = net.predict(X_test)
      score = accuracy_score(y_test, y_pred)
      return score
  
imports: import argparse
  import time
  import numpy
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import StratifiedKFold
  from sklearn.metrics import accuracy_score
  from sklearn.utils import shuffle
  import torch
  from torch import nn
  from skorch.utils import to_device
  from skorch import NeuralNetClassifier
  from skorch.callbacks import EpochScoring
label_desc: Use image node_id 2 for calling a global function with example usage: performance_skorch(X_train, X_test, y_train, y_test, batch_size, device, lr, max_epochs) and returns: score