Categorical feature embedding implementation

In the SC2 LE paper there's this sentence under input pre-processing:
> We embed all feature layers containing categorical values into a continuous space which is
equivalent to using a one-hot encoding in the channel dimension followed by a 1 × 1 convolution.

This raises two question on implementation detail. Let's assume we're dealing with a `64x64` minimap and we want to embed `visibility_map` (4 levels) and `player_relative` (5 levels) features.

1.) What Is embedding dimension? That is, what is the number of kernels used in 1x1 conv?
i.e. if it's `1` then our final (concatenated) output dimensions would be `64x64x2`.

2.) Is embedding done separately per each feature or with one pass-through for all? More specifically:
* 1) one-hot on channel -> concat on channel -> 1x1 conv on all features at the same time.
ex. one-hot to `64x64x4` and `64x64x5` -> concat to `64x64x9` -> 1x1(x2) conv to `64x64x2` output
* 2) one-hot on channel -> 1x1 conv separately per feature -> concat on channel
ex. one-hot to `64x64x4` and `64x64x5` -> 1x1(x1) conv to `64x64x1` and `64x64x1` -> concat to `64x64x2` output

The big difference between the two is that in first case all features influence output channels at the same time.