Can not match the code implementation of embedding propagation layer and the paper

[b3326023]

Excuse me, when I carefully read you code about the function _create_ngcf_embed, I can't understand the code well. Below is the implementation of embedding propagation layer:

for k in range(0, self.n_layers):
    temp_embed = []
    for f in range(self.n_fold):
        # 將每一段adj子矩陣都和ego相乘
        temp_embed.append(tf.sparse_tensor_dense_matmul(A_fold_hat[f], ego_embeddings))

    # sum messages of neighbors.
    side_embeddings = tf.concat(temp_embed, 0)
    # transformed sum messages of neighbors.
    sum_embeddings = tf.nn.leaky_relu(
        tf.matmul(side_embeddings, self.weights['W_gc_%d' % k]) + self.weights['b_gc_%d' % k])


    # bi messages of neighbors.
    bi_embeddings = tf.multiply(ego_embeddings, side_embeddings)
    # transformed bi messages of neighbors.
    bi_embeddings = tf.nn.leaky_relu(
        tf.matmul(bi_embeddings, self.weights['W_bi_%d' % k]) + self.weights['b_bi_%d' % k])

    # non-linear activation.
    ego_embeddings = sum_embeddings + bi_embeddings

    # message dropout.
    ego_embeddings = tf.nn.dropout(
        ego_embeddings, 1 - self.mess_dropout[k])

    # normalize the distribution of embeddings.
    norm_embeddings = tf.math.l2_normalize(ego_embeddings, axis=1)

    all_embeddings += [norm_embeddings]

And here is the corresponding equation in the paper:

Can you explain the relation of the code and the equation, please?
Thank you!