TypeError: You are passing KerasTensor..... when fitting a model

Question

I am trying to finish a project on variational autoencoders (VAE)

I have tried lots of methods and lots of help from the internet but every time I run into a different problem.

Most often it is SHAPE problems, but it can also be like that one: after I gave up, I took a ready-made code (from here: https://www.youtube.com/watch?v=8wrLjnQ7EWQ&ab_channel=DigitalSreeni) and copied it one by one. nd even then it showed me a problem (which I have seen before as well) and there is no answer on the internet (from what I saw):

TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='tf.math.reduce_sum_1/Sum:0', description="created by layer 'tf.math.reduce_sum_1'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as tf.cond, tf.function, gradient tapes, or tf.map_fn. Keras Functional model construction only supports TF API calls that do support dispatching, such as tf.math.add or tf.reshape. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer call and calling that layer on this symbolic input/output.

you can see the full code in the description of the youtube video I have attached

does anyone know what I should do?

Part of the code there, in my opinion, is the problem:

latent_dim = 2

input_img = Input(shape=input_shape, name='encoder_input')
x = Conv2D(32, 3, padding='same', activation='relu')(input_img)
x = Conv2D(64, 3, padding='same', activation='relu',strides=(2, 2))(x)
x = Conv2D(64, 3, padding='same', activation='relu')(x)
x = Conv2D(64, 3, padding='same', activation='relu')(x)
conv_shape = K.int_shape(x) #Shape of conv to be provided to decoder
x = Flatten()(x)
x = Dense(32, activation='relu')(x)
z_mu = Dense(latent_dim, name='latent_mu')(x)   #Mean values of encoded input
z_sigma = Dense(latent_dim, name='latent_sigma')(x)  #Std dev. (variance) of encoded input

def sample_z(args):
  z_mu, z_sigma = args
  eps = K.random_normal(shape=(K.shape(z_mu)[0], K.int_shape(z_mu)[1]))
  return z_mu + K.exp(z_sigma / 2) * eps

z = Lambda(sample_z, output_shape=(latent_dim, ), name='z')([z_mu, z_sigma])
encoder = Model(input_img, [z_mu, z_sigma, z], name='encoder')

decoder_input = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(conv_shape[1]*conv_shape[2]*conv_shape[3], activation='relu')(decoder_input)
x = Reshape((conv_shape[1], conv_shape[2], conv_shape[3]))(x)
x = Conv2DTranspose(32, 3, padding='same', activation='relu',strides=(2, 2))(x)
x = Conv2DTranspose(num_channels, 3, padding='same', activation='sigmoid', name='decoder_output')(x)

decoder = Model(decoder_input, x, name='decoder')
z_decoded = decoder(z)

class CustomLayer(keras.layers.Layer):

    def vae_loss(self, x, z_decoded):
        x = K.flatten(x)
        z_decoded = K.flatten(z_decoded)
        
        # Reconstruction loss (as we used sigmoid activation we can use binarycrossentropy)
        recon_loss = keras.metrics.binary_crossentropy(x, z_decoded)
        
        # KL divergence
        kl_loss = -5e-4 * K.mean(1 + z_sigma - K.square(z_mu) - K.exp(z_sigma), axis=-1)
        return K.mean(recon_loss + kl_loss)

    # add custom loss to the class
    def call(self, inputs):
        x = inputs[0]
        z_decoded = inputs[1]
        loss = self.vae_loss(x, z_decoded)
        self.add_loss(loss, inputs=inputs)
        return x

y = CustomLayer()([input_img, z_decoded])
vae = Model(input_img, y, name='vae')
vae.compile(optimizer='adam', loss=None)
vae.summary()
vae.fit(x_train, None, epochs = 10, batch_size = 32, validation_split = 0.2)