Convolution layers in mesh tensorflow

[taless474]

I like to run the following Keras example deduced from here

# 1D CNN neural network
model_m = Sequential()
model_m.add(Reshape((TIME_PERIODS, num_sensors), input_shape=(input_shape,)))
model_m.add(Conv1D(100, 10, activation='relu', input_shape=(TIME_PERIODS, num_sensors)))
model_m.add(Conv1D(100, 10, activation='relu'))
model_m.add(MaxPooling1D(3))
model_m.add(Conv1D(160, 10, activation='relu'))
model_m.add(Conv1D(160, 10, activation='relu'))
model_m.add(GlobalAveragePooling1D())
model_m.add(Dropout(0.5))
model_m.add(Dense(num_classes, activation='softmax'))

on more than 1 machine (maybe having two nodes of CPUs each having multiple cores). Can I use mesh_tensorflow graphs for convolutional layers? I like to apply both data and spatial parallelism on this example (maybe on a bigger data) on two identical machines. Would you please help me with this? I couldn't find many examples of using TFMesh. Thanks