class_docstr: Construct the embeddings from word, position and token_type embeddings.
      
class_docstr_tok: ["Construct","the","embeddings","from","word",",","position","and","token_type","embeddings","."]
code: def __init__(self, config):
      super().__init__()
  
      self.position_embeddings = nn.Embedding(
          config.max_position_embeddings, config.hidden_size
      )
      # self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
      # self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import torch
  from torch import nn
  from towhee.models.drl.until_module import LayerNorm
  from collections import OrderedDict
label_desc: Use image node_id 1 to create a new CrossEmbeddings object from inherited base classes: nn with example: obj = CrossEmbeddings(config)