Oops, actually use the size of GRU B for training

dnn/training_tf2/train_lpcnet.py

@@ -97,7 +97,7 @@ opt = Adam(lr, decay=decay, beta_2=0.99)
 strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
 
 with strategy.scope():
-    model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=args.grua_size, training=True, quantize=quantize)
+    model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=args.grua_size, rnn_units2=args.grub_size, training=True, quantize=quantize)
     model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics='sparse_categorical_crossentropy')
     model.summary()