class_docstr: Pre-trained Global Vectors for Word Representation (GLOVE) vectors. Uses
  embeddings of dimension 200.
  
  GloVe is an unsupervised learning algorithm for obtaining vector
  representations for words. Training is performed on aggregated global
  word-word co-occurrence statistics from a corpus, and the resulting
  representations showcase interesting linear substructures of the word
  vector space.
  
  
  GloVe: Global Vectors for Word Representation. (Jeffrey Pennington,
      Richard Socher, and Christopher D. Manning. 2014.)
code: def __init__(self, emb_layer_trainable=True):
      glove_path = utils.download_from_s3(
          GloveEmbeddingLayer.EMBEDDING_PATH
      )
      glove_word_list_path = os.path.join(
          glove_path, "glove.wordlist.npy"
      )
      word_list = np.load(glove_word_list_path)
      glove_matrix_path = os.path.join(
          glove_path, "glove.6B.200d.mat.npy"
      )
      embedding_matrix = np.load(glove_matrix_path)
      super().__init__(
          embedding_matrix=embedding_matrix, word_list=word_list
      )
      self.embedding.weight.requires_grad = emb_layer_trainable
  
file_path: TextAttack/textattack/models/helpers/glove_embedding_layer.py
imports: import os
  import numpy
  import torch
  from torch import nn
  from textattack.shared import logger, utils
label_desc: Use image node_id 1 to create a new GloveEmbeddingLayer object from inherited base classes: EmbeddingLayer with example: obj = GloveEmbeddingLayer(emb_layer_trainable)