code: def performance_torch(
      X_train,
      X_test,
      y_train,
      y_test,
      batch_size,
      device,
      lr,
      max_epochs,
  ):
      torch.manual_seed(0)
      model = ClassifierModule()
      model = train_torch(
          model,
          X_train,
          y_train,
          batch_size=batch_size,
          device=device,
          max_epochs=max_epochs,
          lr=lr,
      )
  
      X_test = torch.tensor(X_test).to(device)
      with torch.no_grad():
          y_pred = model(X_test).cpu().numpy().argmax(1)
      return accuracy_score(y_test, y_pred)
  
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 7 for calling a global function with example usage: performance_torch(X_train, X_test, y_train, y_test, batch_size, device, lr, max_epochs) and returns: accuracy_score