A simple lookup table that looks up embeddings in a fixed dictionary and size.

## Usage

```
nnf_embedding(
input,
weight,
padding_idx = NULL,
max_norm = NULL,
norm_type = 2,
scale_grad_by_freq = FALSE,
sparse = FALSE
)
```

## Arguments

- input
(LongTensor) Tensor containing indices into the embedding matrix

- weight
(Tensor) The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size

- padding_idx
(int, optional) If given, pads the output with the embedding vector at

`padding_idx`

(initialized to zeros) whenever it encounters the index.- max_norm
(float, optional) If given, each embedding vector with norm larger than

`max_norm`

is renormalized to have norm`max_norm`

. Note: this will modify`weight`

in-place.- norm_type
(float, optional) The p of the p-norm to compute for the

`max_norm`

option. Default`2`

.- scale_grad_by_freq
(boolean, optional) If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default

`FALSE`

.- sparse
(bool, optional) If

`TRUE`

, gradient w.r.t.`weight`

will be a sparse tensor. See Notes under`nn_embedding`

for more details regarding sparse gradients.