  import fate
import torch

def main(config_file):
    with open(config_file, 'r') as f:
        config = yaml.safe_load(f)

    # Create a Reader to read the training data
    reader = fate.Reader(config['reader'])

    # Create a DataTransform to preprocess the data
    data_transform = fate.DataTransform(config['data_transform'])

    # Create an Intersection to find the common instances between the guest and host data
    intersection = fate.Intersection(config['intersection'])

    # Create a HeteroNN for the model training
    hetero_nn = fate.HeteroNN(config['hetero_nn'])

    # Create an Evaluation for model evaluation
    evaluation = fate.Evaluation(config['evaluation'])

    # Compile the HeteroNN with a specific optimizer and loss function
    optimizer = torch.optim.Adam(hetero_nn.parameters(), lr=0.001)
    loss_fn = torch.nn.CrossEntropyLoss()
    hetero_nn.compile(optimizer=optimizer, loss=loss_fn)

    # Define the guest bottom model
    guest_bottom_model = torch.nn.Linear(5, 10)

    # Define the guest top model
    guest_top_model = torch.nn.Linear(10, 1)

    # Define the host bottom model
    host_bottom_model = torch.nn.Linear(5, 10)

    # Define the interactive layer
    interactive_layer = torch.nn.Linear(10, 1)

    # Add the models to the HeteroNN
    hetero_nn.add_model('guest_bottom', guest_bottom_model)
    hetero_nn.add_model('guest_top', guest_top_model)
    hetero_nn.add_model('host_bottom', host_bottom_model)
    hetero_nn.add_model('interactive', interactive_layer)

    # Compile the HeteroNN
    hetero_nn.compile()

    # Fit the HeteroNN with the training data
    hetero_nn.fit(reader, data_transform, intersection, evaluation)

    # Print the summary of the HeteroNN component
    print(hetero_nn.summary())

if __name__ == '__main__':
    main('config.yaml')