code: def process_text_embedding(
      self, sample_list, embedding_attr="text_embeddings", info=None
  ):
      text_embeddings = []
  
      # Get "text" attribute in case of "text_embeddings" case
      # and "context" attribute in case of "context_embeddings"
      texts = getattr(sample_list, embedding_attr.split("_")[0])
  
      # Get embedding models
      text_embedding_models = getattr(self, embedding_attr)
  
      for text_embedding_model in text_embedding_models:
          # TODO: Move this logic inside
          if isinstance(text_embedding_model, PreExtractedEmbedding):
              embedding = text_embedding_model(sample_list.question_id)
          else:
              embedding = text_embedding_model(texts)
          text_embeddings.append(embedding)
  
      text_embeddding_total = torch.cat(text_embeddings, dim=1)
  
      return text_embeddding_total
  
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 16 for calling the Pythia obj's underlying member method code with example usage: obj.process_text_embedding(sample_list, embedding_attr, info) and returns: text_embeddding_total