 Here is a Python code snippet that uses the FATE library to create a binary classification pipeline with a HeteroNN. The code assumes that you have already installed the FATE library and have the necessary configuration file.

```python
import os
import json
from fate_arch.nn.hetero_nn import HeteroNN
from fate_arch.nn.hetero_nn.hetero_nn_builder import HeteroNNBuilder
from fate_arch.nn.hetero_nn.hetero_nn_config import HeteroNNConfig
from fate_arch.nn.hetero_nn.hetero_nn_utils import (
    guest_traverse_a_tree,
    host_traverse_a_tree,
    find_best_split_guest_and_host,
)
from fate_arch.nn.learner import Learner
from fate_arch.nn.nn_utils import (
    stat_and_transform,
    decompress_and_unpack,
    pack_and_encrypt,
    add_and_sub,
)
from fate_arch.nn.nn_layer import InteractiveLayer
from fate_arch.nn.nn_layer.guest_top_model import GuestTopModel
from fate_arch.nn.nn_layer.host_bottom_model import HostBottomModel
from fate_arch.nn.nn_layer.quadratic_residue_layer import QuadraticResidueLayer
from fate_arch.nn.nn_layer.guest_bottom_model import GuestBottomModel
from fate_arch.nn.nn_layer.host_top_model import HostTopModel
from fate_arch.nn.nn_layer.classification_and_regression_layer import (
    ClassificationAndRegressionLayer,
)
from fate_arch.nn.nn_layer.stat_and_transform_layer import StatAndTransformLayer
from fate_arch.nn.nn_layer.add_and_sub_layer import AddAndSubLayer
from fate_arch.nn.nn_layer.pack_and_encrypt_layer import PackAndEncryptLayer
from fate_arch.nn.nn_layer.decompress_and_unpack_layer import DecompressAndUnpackLayer
from fate_arch.nn.nn_layer.compute_and_aggregate_forwards_layer import (
    ComputeAndAggregateForwardsLayer,
)
from fate_arch.nn.nn_layer.init_sid_and_getfunc_layer import InitSidAndGetFuncLayer
from fate_arch.nn.nn_layer.find_best_split_guest_and_host_layer import (
    FindBestSplitGuestAndHostLayer,
)
from fate_arch.nn.nn_layer.maybe_create_topic_and_replication_layer import (
    MaybeCreateTopicAndReplicationLayer,
)
from fate_arch.nn.nn_layer.check_and_change_lower_layer import CheckAndChangeLowerLayer

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

def create_pipeline(config):
    # Create HeteroNNConfig
    hetero_nn_config = HeteroNNConfig()
    hetero_nn_config.epochs = config["epochs"]
    hetero_nn_config.learning_rate = config["learning_rate"]
    hetero_nn_config.batch_size = config["batch_size"]
    hetero_nn_config.callbacks = config["callbacks"]

    # Create HeteroNNBuilder
    builder = HeteroNNBuilder()

    # Create guest bottom model
    guest_bottom_model = GuestBottomModel()
    builder.add_guest_bottom_model(guest_bottom_model)

    # Create host bottom model
    host_bottom_model = HostBottomModel()
    builder.add_host_bottom_model(host_bottom_model)

    # Create interactive layer
    interactive_layer = InteractiveLayer()
    builder.add_interactive_layer(interactive_layer)

    # Create guest top model
    guest_top_model = GuestTopModel()
    builder.add_guest_top_model(guest_top_model)

    # Create host top model
    host_top_model = HostTopModel()
    builder.add_host_top_model(host_top_model)

    # Create QuadraticResidueLayer
    quadratic_residue_layer = QuadraticResidueLayer()
    builder.add_quadratic_residue_layer(quadratic_residue_layer)

    # Create ClassificationAndRegressionLayer
    classification_and_regression_layer = ClassificationAndRegressionLayer()
    builder.add_classification_and_regression_layer(
        classification_and_regression_layer
    )

    # Create StatAndTransformLayer
    stat_and_transform_layer = StatAndTransformLayer()
    builder.add_stat_and_transform_layer(stat_and_transform_layer)

    # Create AddAndSubLayer
    add_and_sub_layer = AddAndSubLayer()
    builder.add_add_and_sub_layer(add_and_sub_layer)

    # Create PackAndEncryptLayer
    pack_and_encrypt_layer = PackAndEncryptLayer()
    builder.add_pack_and_encrypt_layer(pack_and_encrypt_layer)

    # Create DecompressAndUnpackLayer
    decompress_and_unpack_layer = DecompressAndUnpackLayer()
    builder.add_decompress_and_unpack_layer(decompress_and_unpack_layer)

    # Create ComputeAndAggregateForwardsLayer
    compute_and_aggregate_forwards_layer = ComputeAndAggregateForwardsLayer()
    builder.add_compute_and_aggregate_forwards_layer(
        compute_and_aggregate_forwards_layer
    )

    # Create InitSidAndGetFuncLayer
    init_sid_and_getfunc_layer = InitSidAndGetFuncLayer()
    builder.add_init_sid_and_getfunc_layer(init_sid_and_getfunc_layer)

    # Create FindBestSplitGuestAndHostLayer
    find_best_split_guest_and_host_layer = FindBestSplitGuestAndHostLayer()
    builder.add_find_best_split_guest_and_host_layer(
        find_best_split_guest_and_host_layer
    )

    # Create MaybeCreateTopicAndReplicationLayer
    maybe_create_topic_and_replication_layer = MaybeCreateTopicAndReplicationLayer()
    builder.add_maybe_create_topic_and_replication_layer(
        maybe_create_topic_and_replication_layer
    )

    # Create CheckAndChangeLowerLayer
    check_and_change_lower_layer = CheckAndChangeLowerLayer()
    builder.add_check_and_change_lower_layer(check_and_change_lower_layer)

    # Build HeteroNN
    hetero_nn = builder.build()

    return hetero_nn

def main(config_file):
    config = load_config(config_file)
    hetero_nn = create_pipeline(config)

    # Create Learner
    learner = Learner()

    # Set HeteroNN to the learner
    learner.set_model(hetero_nn)

    # Compile the learner
    learner.compile()

    # Fit the learner
    learner.fit()

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

if __name__ == "__main__":
    main("config.json")
```

This code defines a main function that loads a configuration file, creates a HeteroNN pipeline, sets it as the model for a Learner, compiles and fits the Learner, and prints the summary of the HeteroNN component. The configuration file should contain the necessary parameters for the HeteroNN, such as the number of epochs, learning rate, batch size, and callback parameters.