code: def test_lenet_distributed_ndarray(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,
          validation_data=(X_train, y_train),
          is_distributed=True,
      )
      model.predict(X_train, is_distributed=True).collect()
      model.evaluate(X_train, y_train, is_distributed=True)
      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 4 for calling the TestBackend obj's underlying member method code with example usage: obj.test_lenet_distributed_ndarray() without return types