code: def build_model(input_shape):
      model = Sequential()
      model.add(Conv2D(32, 3, 3, input_shape=input_shape))
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=[2, 2]))
  
      model.add(Conv2D(32, 3, 3))
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=[2, 2]))
  
      model.add(Conv2D(64, 3, 3))
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=[2, 2]))
  
      model.add(Flatten())
      model.add(Dense(64))
      model.add(Activation("relu"))
      model.add(Dropout(0.5))
      model.add(Dense(1))
      model.add(Activation("sigmoid"))
  
      return model
  
file_path: BigDL/python/dllib/examples/keras/image_classification.py
imports: from optparse import OptionParser
  from pyspark.sql.functions import col, udf
  from pyspark.sql.types import IntegerType, StringType
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.optim.optimizer import *
  from bigdl.dllib.keras.objectives import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras.layers import *
  from bigdl.dllib.keras.models import *
  from bigdl.dllib.nnframes import *
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(input_shape) and returns: model