import tensorflow as tf

class VideoCompressionModel(tf.keras.Model):
    def __init__(self, NUM_CHANNELS=3):
        super(VideoCompressionModel, self).__init__()

        # Encoder layers
        self.encoder = tf.keras.Sequential([
            tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(None, None, NUM_CHANNELS)),
            # Add more encoder layers as needed
        ])

        # Decoder layers
        self.decoder = tf.keras.Sequential([
            tf.keras.layers.Conv2DTranspose(32, (3, 3), activation='relu', padding='same'),
            # Add more decoder layers as needed
            tf.keras.layers.Conv2D(NUM_CHANNELS, (3, 3), activation='sigmoid', padding='same')  # Output layer for video frames
        ])

    def call(self, inputs):
        # Encoding the video frames
        compressed_representation = self.encoder(inputs)

        # Decoding to generate compressed video frames
        reconstructed_frames = self.decoder(compressed_representation)

        return reconstructed_frames