code: def train():
      X, y = make_classification(
          1000, 20, n_informative=10, random_state=0
      )
      X = X.astype(np.float32)
      y = y.astype(np.int64)
  
      module = make_classifier(input_units=20)
  
      net = NeuralNetClassifier(
          module,
          max_epochs=10,
          lr=0.1,
          callbacks=[TriggerKeyError(), PrintMemory()],
          device="cuda",
      )
  
      return net.fit(X, y)
  
file_path: skorch/examples/benchmarks/history.py
imports: from pprint import pprint
  import time
  import numpy
  from sklearn.datasets import make_classification
  import torch
  from skorch import NeuralNetClassifier
  from skorch.callbacks import Callback
  from skorch.toy import make_classifier
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: net