/// <summary>
/// A simple lookup table that stores embeddings of a fixed dictionary and size.
/// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.
/// </summary>
/// <param name="x">An input tensor of arbitrary shape.</param>
/// <param name="num_embeddings">Size of the dictionary of embeddings, the vocabulary size.</param>
/// <param name="embedding_dims">The size of each embedding vector</param>
/// <param name="padding_idx">If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.</param>
/// <param name="max_norm">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>
/// <param name="norm_type">The p of the p-norm to compute for the max_norm option. Default 2.</param>
/// <param name="scale_grad_by_freq">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>
/// <param name="sparse">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>
/// <returns></returns>
/// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>
static public TorchTensor Embedding(TorchTensor x, long num_embeddings, long embedding_dims, long?padding_idx = null, double?max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, bool sparse = false)
{
using (var d = Modules.Embedding(num_embeddings, embedding_dims, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)) {
return(d.forward(x));
}
}