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.
docstr: Args:
      embedding_size: Dimension of the dense embedding.
      kwargs: additional params
docstr_tok: ["Args",":","embedding_size",":","Dimension","of","the","dense","embedding",".","kwargs",":","additional","params"]
code: def __init__(self, embedding_size: int = 100, **kwargs: Any):
      """
  
      Args:
          embedding_size: Dimension of the dense embedding.
          kwargs: additional params
      """
      self.embedding_size: int = embedding_size
      super(BareEmbedding, self).__init__(
          embedding_size=embedding_size, **kwargs
      )
  
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 1 to create a new BareEmbedding object from inherited base classes: ABCEmbedding with example: obj = BareEmbedding(embedding_size)