code: def get_classifier_df(self):
      data = self.sc.parallelize(
          [
              ((2.0, 1.0), 1.0),
              ((1.0, 2.0), 2.0),
              ((2.0, 1.0), 1.0),
              ((1.0, 2.0), 2.0),
          ]
      )
  
      schema = StructType(
          [
              StructField(
                  "features", ArrayType(DoubleType(), False), False
              ),
              StructField("label", DoubleType(), False),
          ]
      )
      df = self.sqlContext.createDataFrame(data, schema)
      return df
  
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 4 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.get_classifier_df() and returns: df