code: def kmodel_graph_1_layer():
      input1 = Input(shape=(3,))
      dense = Dense(2)(input1)
      kmodel = Model(input=input1, output=dense)
      kmodel.compile(
          loss="categorical_crossentropy",
          optimizer=RMSprop(),
          metrics=["accuracy"],
      )
      input_data = np.random.sample([1, 3])
      output_data = np.random.sample([1, 2])
      return kmodel, input_data, output_data
  
imports: from __future__ import print_function
  import numpy
  from keras.models import Sequential, Model
  from keras.layers.core import *
  from keras.layers.convolutional import *
  from keras.layers import Dense, Dropout, Input
  from keras.optimizers import RMSprop
  from bigdl.dllib.utils.common import *
  from bigdl.dllib.keras.converter import *
  from bigdl.dllib.keras.converter import WeightLoader, WeightsConverter
  import numpy
  from unittest import TestCase
  import keras
  from bigdl.dllib.nn.keras.keras_utils import *
label_desc: Use image node_id 1 for calling the TestModels obj's underlying member method code with example usage: obj.kmodel_graph_1_layer() and returns: kmodel,  input_data,  output_data