code: def get_embedding(self, cls, **embedding_kwargs):
      vector_dim = len(self.vectors[0])
      embedding_kwargs["vocab_size"] = self.get_size()
  
      embedding_dim = embedding_kwargs["embedding_dim"]
      embedding_kwargs["embedding_dim"] = vector_dim
  
      embedding = None
  
      if cls == torch.nn.Embedding:
          embedding = torch.nn.Embedding(self.get_size(), vector_dim)
      else:
          embedding = cls(**embedding_kwargs)
  
      if hasattr(embedding, "embedding"):
          embedding.embedding = torch.nn.Embedding.from_pretrained(
              self.vectors, freeze=False
          )
      else:
          embedding = torch.nn.Embedding.from_pretrained(
              self.vectors, freeze=False
          )
  
      if vector_dim == embedding_dim:
          return embedding
      else:
          return torch.nn.Sequential(
              [embedding, torch.nn.Linear(vector_dim, embedding_dim)]
          )
  
imports: import logging
  import os
  from collections import defaultdict
  import numpy
  import torch
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.distributed import is_main, synchronize
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_absolute_path
  from torchtext import vocab
label_desc: Use image node_id 14 for calling the BaseVocab obj's underlying member method code with example usage: obj.get_embedding(cls) and returns: embedding, torch