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def upsample(self, filters, size, apply_dropout=False):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = tf.keras.Sequential()
    result.add(
    tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
                                    padding='same',
                                    kernel_initializer=initializer,
                                    use_bias=False))

    result.add(tf.keras.layers.BatchNormalization())

    if apply_dropout:
        result.add(tf.keras.layers.Dropout(0.5))

    result.add(tf.keras.layers.ReLU())

    return result

def downsample(self, filters, size, apply_batchnorm=True):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = tf.keras.Sequential()
    result.add(
        tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
                                kernel_initializer=initializer, use_bias=False))

    if apply_batchnorm:
    result.add(tf.keras.layers.BatchNormalization())

    result.add(tf.keras.layers.LeakyReLU())

    return result


def build_generator(self): 
    inputs = Input(shape=self.noise_shape, name='generator_noise')
    
    # pix2pix concept
    down_stack = [
        downsample(64, 3, apply_batchnorm=False),
        downsample(128, 3),
        downsample(256, 3),
        downsample(512, 3),
        downsample(512, 3),
    ]

    up_stack = [
        upsample(512, 4, apply_dropout=True),  # (batch_size, 2, 2, 1024)
        upsample(512, 4, apply_dropout=True),  # (batch_size, 2, 2, 1024)
        upsample(256, 4, apply_dropout=True),  # (batch_size, 4, 4, 1024)
        upsample(128, 4, apply_dropout=True),  # (batch_size, 8, 8, 1024)
    ]

    initializer = tf.random_normal_initializer(0., 0.02)
    last = tf.keras.layers.Conv2DTranspose(3, 4,
                                            strides=2,
                                            padding='same',
                                            kernel_initializer=initializer,
                                            activation='tanh')

    x = inputs
    skips = []
    for down in down_stack:
        x = down(x)
        skips.append(x)

    skips = reversed(skips[:-1])

    # Upsampling and establishing the skip connections
    for up, skip in zip(up_stack, skips):
        x = up(x)
        x = tf.keras.layers.Concatenate()([x, skip])

    x = last(x)
    generator = Model(inputs=inputs, outputs=x, name='generator')
    generator.summary()
    return generator