code: def test_lenet_local(self):
      kmodel, X_train, y_train = TestModels.kmodel_seq_lenet_mnist()
      self.modelTest(X_train, kmodel, dump_weights=True)
      kmodel.compile(
          loss="categorical_crossentropy",
          optimizer="adam",
          metrics=["accuracy"],
      )
      model = with_bigdl_backend(kmodel)
  
      model.fit(X_train, y_train, batch_size=4, nb_epoch=2)
      model.predict(X_train)
      # TODO: support evaluate for local mode.
      # model.evaluate(X_train, y_train)
      print(model)
  
file_path: BigDL/python/dllib/test/bigdl/nn/test_backend.py
imports: from __future__ import print_function
  from keras.layers import *
  import pytest
  from keras.applications import *
  from bigdl.dllib.keras.converter import *
  from bigdl.dllib.nn.keras.backend import *
  from test.bigdl.test_utils import BigDLTestCase, TestModels
label_desc: Use image node_id 3 for calling the TestBackend obj's underlying member method code with example usage: obj.test_lenet_local() without return types