code: def _build_word_embedding(self):
      assert len(self._datasets) > 0
      text_processor = registry.get(
          self._datasets[0] + "_text_processor"
      )
      vocab = text_processor.vocab
      self.word_embedding = vocab.get_embedding(
          torch.nn.Embedding, embedding_dim=300
      )
  
imports: import copy
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import ImageFeatureEmbedding, MultiHeadImageFeatureEmbedding, PreExtractedEmbedding, TextEmbedding
  from mmf.modules.layers import ClassifierLayer, ModalCombineLayer
  from mmf.utils.build import build_image_encoder
  from torch import nn
label_desc: Use image node_id 5 for calling the Pythia obj's underlying member method code with example usage: obj._build_word_embedding() without return types