class_docstr: BareEmbedding is a random init `tf.keras.layers.Embedding` layer for text sequence embedding,
  which is the defualt embedding class for kashgari models.
code: def build_embedding_model(
      self,
      *,
      vocab_size: int = None,
      force: bool = False,
      **kwargs: Dict,
  ) -> None:
      if self.embed_model is None or force:
          input_tensor = L.Input(shape=(None,), name=f"input")
          layer_embedding = L.Embedding(
              vocab_size,
              self.embedding_size,
              mask_zero=True,
              name=f"layer_embedding",
          )
  
          embedded_tensor = layer_embedding(input_tensor)
          self.embed_model = keras.Model(input_tensor, embedded_tensor)
  
file_path: Kashgari/kashgari/embeddings/bare_embedding.py
imports: from typing import Dict, Any, Optional
  from tensorflow import keras
  from kashgari.embeddings.abc_embedding import ABCEmbedding
label_desc: Use image node_id 3 for calling the BareEmbedding obj's underlying member method code with example usage: obj.build_embedding_model() without return types