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Encoder (Dense connection without flatten) & Decoder input does not have reshape

@VeeranjaneyuluToka

Please let me know if my understanding is correct here or not?

Encoder - Dense connection without Flatten

class ConvAutoEncoder(HyperModel):
    def __init__(self, input_shape, is_deeper=False):
--snip--
    """ encoder """
    def encoder(self, input_img, hp):

        conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) #224 x 224 x 32
        conv1 = tf.keras.layers.BatchNormalization()(conv1)
        conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
--snip--
        conv6 = tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
        conv6 = tf.keras.layers.BatchNormalization()(conv6)
        conv6 = tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same')(conv6)
        conv6 = tf.keras.layers.BatchNormalization()(conv6)

        # Comment (asinha) : conv6 output would be (14,14,512)
        # However, we do not flatten this before building a dense below this
        # would this be correct?

        dense = tf.keras.layers.Dense(16, activation='relu', name='encoder')(conv6)

Decoder : Input does not reshape the encoded input before applying a Conv layer

class ConvAutoEncoder(HyperModel):
    def __init__(self, input_shape, is_deeper=False):
--snip--
    """ decoder """
    def decoder(self, dense, hp):

        # Comment (asinha) : If we are getting a dense input, which I am believing a vector if 16 (as 
        # per last layer at encoder), needs to be reshaped before we apply a CONV2D(256)
        # Also I do not understand why do we have #56x56x128 added ahead of conv5?

        conv5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same')(dense) #56 x 56 x 128
        conv5 = tf.keras.layers.BatchNormalization()(conv5)
        conv5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same')(conv5)
        conv5 = tf.keras.layers.BatchNormalization()(conv5)

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