def METHOD_NAME(self):
    K.clear_session()
    vocab_size = 50
    max_len = 20
    embedding_size = 100
    model = Sequential()
    model.add(Embedding(input_dim=vocab_size, input_length=max_len, output_dim=embedding_size))
    model.add(SpatialDropout1D(0.2))
    model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu'))
    model.add(GlobalMaxPool1D())
    model.add(Dense(units=30, activation='softmax'))
    data = np.random.rand(1000, max_len).astype(np.float32)
    expected = model.predict(data)
    onnx_model = mock_keras2onnx.convert_keras(model, model.name)
    self.assertTrue(
        run_keras_and_ort(onnx_model.graph.name, onnx_model, model, data, expected, self.model_files))