def load_pytorch_embedding_layer(pretrained_embedding: str, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
"""
:param pretrained_embedding:
:param cache_dir: the directory for storing cached models
:return: an pytorch Embedding module and a list id2word
"""
word_embeddings_available(pretrained_embedding, can_use_subword=False)
import torch
from torch.nn import Embedding
word_vectors = load_wv_with_gensim(pretrained_embedding, cache_dir=cache_dir, verbose=verbose)
weights = torch.FloatTensor(word_vectors.vectors)
return Embedding.from_pretrained(weights), word_vectors.index2word
