def cifar10ClassifierTransfer(input_img, conv2_high):

  # Add additional inputs to

  conv1_1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)
  conv1_2 = BatchNormalization()(conv1_1)
  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)
  conv2_high = conv2_high[:,:,:,[1,2,3]]
  concat_layer = keras.layers.merge.Concatenate(axis=3)([conv2_1,conv2_high])

  flat = Flatten()(concat_layer)
  out = Dense(num_classes, activation='softmax')(flat)

  return out

TypeError: Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got [1, 2, 3]