class_docstr: TransformerEmbedding is based on bert4keras.
  The embeddings itself are wrapped into our simple embedding interface so that they can be used like any other embedding.
class_docstr_tok: ["TransformerEmbedding","is","based","on","bert4keras",".","The","embeddings","itself","are","wrapped","into","our","simple","embedding","interface","so","that","they","can","be","used","like","any","other","embedding","."]
code: def to_dict(self) -> Dict[str, Any]:
      info_dic = super(TransformerEmbedding, self).to_dict()
      info_dic["config"]["vocab_path"] = self.vocab_path
      info_dic["config"]["config_path"] = self.config_path
      info_dic["config"]["checkpoint_path"] = self.checkpoint_path
      info_dic["config"]["model_type"] = self.model_type
      return info_dic
  
file_path: Kashgari/kashgari/embeddings/transformer_embedding.py
imports: import codecs
  import json
  from typing import Dict, List, Any, Optional
  from bert4keras.models import build_transformer_model
  from kashgari.embeddings.abc_embedding import ABCEmbedding
  from kashgari.logger import logger
label_desc: Use image node_id 1 for calling the TransformerEmbedding obj's underlying member method code with example usage: obj.to_dict() and returns: info_dic