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):
      glove_path = download_if_needed(GloveEmbedding.EMBEDDING_PATH)
      # glove_path = download_if_needed(GloveEmbedding.EMBEDDING_PATH)
      glove_word_list_path = os.path.join(
          glove_path, "glove200/glove.wordlist.npy"
      )
      word_list = np.load(glove_word_list_path)
      glove_matrix_path = os.path.join(
          glove_path, "glove200/glove.6B.200d.mat.npy"
      )
      embedding_matrix = np.load(glove_matrix_path).astype(np.float32)
      super().__init__(
          embedding_matrix=embedding_matrix, word_list=word_list
      )
  
file_path: textflint/textflint/input/model/test_model/glove_embedding.py
imports: import os
  import numpy
  import textattack
  from ....common.utils import logger, download_if_needed
label_desc: Use image node_id 1 to create a new GloveEmbedding object from inherited base classes: Embedding with example: obj = GloveEmbedding()