text: import numpy as np
  from sklearn.ensemble import RandomForestClassifier
  from hummingbird.ml import convert
  
  # Create some random data for binary classification
  from sklearn import datasets
  X, y = datasets.make_classification(n_samples=100000, n_features=28)
  
  # Create and train a model (scikit-learn RandomForestClassifier in this case)
  skl_model = RandomForestClassifier(n_estimators=1000, max_depth=10)
  skl_model.fit(X, y)
  
  # Using Hummingbird to convert the model to PyTorch
  model = convert(skl_model, 'pytorch')
  print(type(model))
  
  model.to('cuda')