code: def test_NNEstimator_works_with_VectorAssembler_multi_input(self):
      if self.sc.version.startswith("2"):
          from pyspark.ml.linalg import Vectors
          from pyspark.ml.feature import VectorAssembler
          from pyspark.sql import SparkSession
  
          spark = SparkSession.builder.getOrCreate()
  
          df = spark.createDataFrame(
              [
                  (
                      1,
                      35,
                      109.0,
                      Vectors.dense([2.0, 5.0, 0.5, 0.5]),
                      1.0,
                  ),
                  (
                      2,
                      58,
                      2998.0,
                      Vectors.dense([4.0, 10.0, 0.5, 0.5]),
                      2.0,
                  ),
                  (
                      3,
                      18,
                      123.0,
                      Vectors.dense([3.0, 15.0, 0.5, 0.5]),
                      1.0,
                  ),
              ],
              ["user", "age", "income", "history", "label"],
          )
  
          assembler = VectorAssembler(
              inputCols=["user", "age", "income", "history"],
              outputCol="features",
          )
  
          df = assembler.transform(df)
  
          x1 = ZLayer.Input(shape=(1,))
          x2 = ZLayer.Input(shape=(2,))
          x3 = ZLayer.Input(
              shape=(
                  2,
                  2,
              )
          )
  
          user_embedding = ZLayer.Embedding(5, 10)(x1)
          flatten = ZLayer.Flatten()(user_embedding)
          dense1 = ZLayer.Dense(2)(x2)
          gru = ZLayer.LSTM(4, input_shape=(2, 2))(x3)
  
          merged = ZLayer.merge([flatten, dense1, gru], mode="concat")
          zy = ZLayer.Dense(2)(merged)
  
          zmodel = ZModel([x1, x2, x3], zy)
          criterion = ClassNLLCriterion()
          classifier = (
              NNClassifier(zmodel, criterion, [[1], [2], [2, 2]])
              .setOptimMethod(Adam())
              .setLearningRate(0.1)
              .setBatchSize(2)
              .setMaxEpoch(10)
          )
  
          nnClassifierModel = classifier.fit(df)
          print(nnClassifierModel.getBatchSize())
          res = nnClassifierModel.transform(df).collect()
  
imports: import errno
  import shutil
  import pytest
  from bigdl.dllib.nn.criterion import *
  from bigdl.dllib.nn.layer import *
  from bigdl.dllib.optim.optimizer import *
  from numpy.testing import assert_allclose
  from pyspark.ml import Pipeline, PipelineModel
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.tuning import ParamGridBuilder
  from pyspark.sql.types import *
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.feature.common import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras import layers
  from bigdl.dllib.keras.models import Model
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.dllib.nnframes import *
  from bigdl.dllib.utils.tf import *
  from pyspark.sql.functions import array
  from pyspark.ml.linalg import DenseVector, VectorUDT
  from pyspark.sql.functions import udf, array
label_desc: Use image node_id 20 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_NNEstimator_works_with_VectorAssembler_multi_input() without return types