class_docstr: A layer of a model that replaces word IDs with their embeddings.
  
  This is a useful abstraction for any nn.module which wants to take
  word IDs (a sequence of text) as input layer but actually manipulate
  words' embeddings.
  
  Requires some pre-trained embedding with associated word IDs.
code: def forward(self, input):
      return self.embedding(input)
  
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 2 for calling the EmbeddingLayer obj's underlying member method code with example usage: obj.forward(input) and returns: self