def METHOD_NAME():
    if framework in ("keras", "kerastf"):
        import tensorflow as tf
        from tensorflow.keras import layers, Sequential
        if tf.__version__[0] == "2":
            tf.compat.v1.disable_eager_execution()
        model = Sequential()
        model.add(layers.MaxPooling2D(pool_size=(2, 2), input_shape=(22, 22, 1)))
        model.add(layers.Flatten())
        model.add(layers.Dense(2, activation="softmax"))
        model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
        classifier = KerasClassifier(model=model, clip_values=(0, 1), use_logits=False)
    elif framework == "tensorflow2":
        import tensorflow as tf
        from tensorflow.keras import layers, Sequential
        model = Sequential()
        model.add(layers.MaxPooling2D(pool_size=(2, 2), input_shape=(22, 22, 1)))
        model.add(layers.Flatten())
        model.add(layers.Dense(2, activation="softmax"))
        loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=False)
        optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
        classifier = TensorFlowV2Classifier(
            model,
            nb_classes=2,
            input_shape=(22, 22, 1),
            loss_object=loss_object,
            optimizer=optimizer,
        )
    elif framework == "pytorch":
        import torch
        model = torch.nn.Sequential(torch.nn.MaxPool2d(2, 2), torch.nn.Flatten(), torch.nn.Linear(121, 2))
        criterion = torch.nn.CrossEntropyLoss()
        optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
        classifier = PyTorchClassifier(
            model,
            loss=criterion,
            optimizer=optimizer,
            input_shape=(1, 22, 22),
            nb_classes=2,
        )
    else:
        classifier = None
    return classifier