Hi Guys,

I had a matrix factorization network defined by the following graph. I could get the weights of the embedding layers. Say now I had saved the weights. If I reloaded the weights to a new instance of the same network graph, can I fix certain weights (eg, get only some weights being updated)?

For example, when training the new network instance, I actually just want to update the last weight of the 8 latent_dim weights, and leave the first 7 fixed.

Thanks!

```
latent_dim = 8
y_true = mx.symbol.Variable("label")
user = mx.symbol.Variable("member")
user = mx.symbol.Embedding(name='member_embedding', data=user, input_dim=n_users, output_dim=latent_dim)
book = mx.symbol.Variable("book")
book = mx.symbol.Embedding(name='book_embedding', data=book, input_dim=n_items, output_dim=latent_dim)
dot = user * book
dot = mx.symbol.sum_axis(dot, axis=1)
dot = mx.symbol.Flatten(dot)
dot = 1 - dot
return mx.symbol.LinearRegressionOutput(data=dot, label=y_true)
```