def METHOD_NAME(self, x, a):
    # This will be a list of channels diffused features.
    # Each diffused feature is a (batch, n_nodes, 1) tensor.
    # Later we will concat all the features to get one
    # (batch, n_nodes, channels) diffused graph signal
    diffused_features = []
    # Iterating over all channels diffusion filters
    for diffusion in self.filters:
        diffused_feature = diffusion((x, a))
        diffused_features.append(diffused_feature)
    return tf.concat(diffused_features, -1)