code: def kmodel_seq_lenet_mnist():
      # Part of the code is from keras example
      # assuming channel first
      input_shape = [1, 28, 28]
      b_input_shape = input_shape[:]
      nb_samples = 4
      b_input_shape.insert(0, nb_samples)
      nb_classes = 10
  
      # input image dimensions
      img_rows, img_cols = 28, 28
      # number of convolutional filters to use
      nb_filters = 32
      # size of pooling area for max pooling
      pool_size = (2, 2)
      # convolution kernel size
      kernel_size = (3, 3)
  
      model = Sequential()
  
      model.add(
          Convolution2D(
              nb_filters,
              kernel_size[0],
              kernel_size[1],
              border_mode="valid",
              input_shape=input_shape,
          )
      )
      model.add(Activation("relu"))
      model.add(
          Convolution2D(nb_filters, kernel_size[0], kernel_size[1])
      )
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=pool_size))
      model.add(Dropout(0.0))
  
      model.add(Flatten())
      model.add(Dense(128))
      model.add(Activation("relu"))
      model.add(Dropout(0.0))
      model.add(Dense(nb_classes))
      model.add(Activation("softmax"))
  
      model.compile(
          loss="categorical_crossentropy",
          optimizer="Adagrad",
          metrics=["accuracy"],
      )
      input_data = np.random.random_sample(b_input_shape)
      output_data = np.random.randint(1, 5, [nb_samples, 1])
      return model, 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 3 for calling the TestModels obj's underlying member method code with example usage: obj.kmodel_seq_lenet_mnist() and returns: model,  input_data,  output_data