encoder_input = Input(shape=self.input_dim, name='encoder_input')

        x = encoder_input

        for i in range(self.n_layers_encoder):
            conv_layer = Conv2D(
                filters = self.encoder_conv_filters[i]
                , kernel_size = self.encoder_conv_kernel_size[i]
                , strides = self.encoder_conv_strides[i]
                , padding = 'same'
                , name = 'encoder_conv_' + str(i)
                )

            x = conv_layer(x)

            if self.use_batch_norm:
                x = BatchNormalization()(x)

            x = LeakyReLU()(x)

            if self.use_dropout:
                x = Dropout(rate = 0.25)(x)

        shape_before_flattening = K.int_shape(x)[1:]

        x = Flatten()(x)
        #<1> Instead of connecting the flattened layer 
        # directly to the 2D latent space, we connect 
        # it to layers mu and log_var .
        self.mu = Dense(self.z_dim, name='mu')(x)
        self.log_var = Dense(self.z_dim, name='log_var')(x)
         
        #<2> The Keras model that outputs the values 
        # of mu and log_var for a given input image.
        self.encoder_mu_log_var = Model(encoder_input, (self.mu, self.log_var))

        def sampling(args):
            mu, log_var = args
            epsilon = K.random_normal(shape=K.shape(mu), mean=0., stddev=1.)
            return mu + K.exp(log_var / 2) * epsilon
        #This Lambda layer samples a point z in 
        # the latent space from the normal distribution 
        #defined by the parameters mu and log_var .
        encoder_output = Lambda(sampling, name='encoder_output')([self.mu, self.log_var])

        self.encoder = Model(encoder_input, encoder_output)```