The input type is MultiArray when keras model is converted to CoreML

I am trying to convert a keras model to CoreML model for a classification task using cifar10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html). However, in the converted model shows `MultiArray` input. How do I change this to `Image <BGR, 32, 32>` or something like `CVPixelBuffer`?

I checked this question: https://forums.developer.apple.com/message/237516#237516

However, I am already using the shape (height, width, depth / channels). You can see the complete code below(Ref:https://cambridgespark.com/content/tutorials/convolutional-neural-networks-with-keras/index.html):

from keras.datasets import cifar10
from keras.models import Model
from keras.layers import Input, Convolution2D, MaxPooling2D, Dense, Dropout, Flatten
from keras.utils import np_utils
import numpy as np
import coremltools

np.random.seed(1234)

batch_size = 32
num_epochs = 1
kernel_size = 3
pool_size = 2
conv_depth_1 = 32
conv_depth_2 = 64
drop_prob_1 = 0.25
drop_prob_2 = 0.5
hidden_size = 512

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
num_train, height, width, depth = X_train.shape
num_test = X_test.shape[0]
num_classes = 10
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= np.max(X_train)
X_test /= np.max(X_test)
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)

data = Input(shape=(height, width, depth))
conv_1 = Convolution2D(conv_depth_1, (kernel_size, kernel_size), padding='same', activation='relu')(data)
conv_2 = Convolution2D(conv_depth_1, (kernel_size, kernel_size), padding='same', activation='relu')(conv_1)
pool_1 = MaxPooling2D(pool_size=(pool_size, pool_size))(conv_2)
drop_1 = Dropout(drop_prob_1)(pool_1)
conv_3 = Convolution2D(conv_depth_2, (kernel_size, kernel_size), padding='same', activation='relu')(drop_1)
conv_4 = Convolution2D(conv_depth_2, (kernel_size, kernel_size), padding='same', activation='relu')(conv_3)
pool_2 = MaxPooling2D(pool_size=(pool_size, pool_size))(conv_4)
drop_2 = Dropout(drop_prob_1)(pool_2)
flat = Flatten()(drop_2)
hidden = Dense(hidden_size, activation='relu')(flat)
drop_3 = Dropout(drop_prob_2)(hidden)
out = Dense(num_classes, activation='softmax')(drop_3)

model = Model(inputs=data, outputs=out)

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
model.fit(X_train, y_train,         
          batch_size=batch_size, epochs=num_epochs,
          verbose=1, validation_split=0.1)

loss, accuracy = model.evaluate(X_test, y_test, verbose=1)
print ("\nTest Loss: {loss} and Test Accuracy: {acc}\n".format(loss = loss, acc = accuracy))

coreml_model = coremltools.converters.keras.convert(model, input_names='data', image_input_names='data')
coreml_model.save('my_model.mlmodel')