 Here's a simplified example of how you might set up a binary classification task using the FATE (Fairness, Accountability, and Transparency in Machine Learning for All) library's HeteroNN. Note that this is a basic example and might need adjustments based on your specific use case.

```python
import os
import json
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model
from fate_flow.api.nn_framework.hetero_nn import HeteroNN
from fate_flow.api.nn_framework.hetero_nn.hetero_nn_builder import HeteroNNBuilder

def create_guest_bottom_model():
    guest_bottom_input = Input(shape=(784,))
    x = Dense(128, activation='relu')(guest_bottom_input)
    return Model(inputs=guest_bottom_input, outputs=x)

def create_host_bottom_model():
    host_bottom_input = Input(shape=(10,))
    x = Dense(64, activation='relu')(host_bottom_input)
    return Model(inputs=host_bottom_input, outputs=x)

def create_interactive_layer():
    guest_bottom_output = Input(shape=(128,))
    host_bottom_output = Input(shape=(64,))
    x = tf.keras.layers.concatenate([guest_bottom_output, host_bottom_output])
    x = Dense(128, activation='relu')(x)
    return Model([guest_bottom_output, host_bottom_output], x)

def create_guest_top_model():
    x = Dense(1, activation='sigmoid')
    return Model(inputs=create_interactive_layer().output, outputs=x)

def create_hetero_nn(config):
    builder = HeteroNNBuilder()
    builder.add_guest_bottom_model(create_guest_bottom_model())
    builder.add_host_bottom_model(create_host_bottom_model())
    builder.add_interactive_layer(create_interactive_layer())
    builder.add_guest_top_model(create_guest_top_model())

    builder.set_num_epochs(config['num_epochs'])
    builder.set_learning_rate(config['learning_rate'])
    builder.set_batch_size(config['batch_size'])
    builder.set_callbacks(config['callbacks'])

    return builder.build()

def main(config_file):
    with open(config_file) as f:
        config = json.load(f)

    hetero_nn = create_hetero_nn(config)
    hetero_nn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    # Assuming you have a function to load and preprocess data
    (x_train, y_train), (x_test, y_test) = load_and_preprocess_data()

    # Assuming you have a function to intersect data
    x_train_intersected, x_test_intersected = intersect_data(x_train, x_test)

    hetero_nn.fit(x_train_intersected, y_train, validation_data=(x_test_intersected, y_test))

    print(hetero_nn.summary())

if __name__ == "__main__":
    main(os.path.join(os.getcwd(), 'config.json'))
```

This code defines functions for creating the guest bottom model, host bottom model, interactive layer, and guest top model. The `create_hetero_nn` function builds the HeteroNN with the specified parameters. The `main` function loads the configuration, preprocesses the data, intersects the data, compiles and fits the HeteroNN, and prints the summary of the HeteroNN component. The configuration file should contain the necessary parameters for the HeteroNN.