Embedding Layer for Keras with weights from gensim Word2Vec (or FastText, why not?)

keras_word2vec_embedding.py

def word2vec_embedding_layer(embeddings_path='data/weights.npz', max_review_length=150):
    weights = load_weights(embeddings_path)
    layer = Embedding(input_dim=weights.shape[0],
                      output_dim=weights.shape[1],
                      input_length=max_review_length,
                      weights=[weights])
    return layer
  
# How to make 'data/weights.npz'? What is load_weights()?
# See https://gist.github.com/nokados/d5cfec00bc194822f89dff556ff62b29