class_docstr: Intent Extraction model base class (using tf.keras)
class_docstr_tok: ["Intent","Extraction","model","base","class","(","using","tf.keras",")"]
code: def _create_input_embed(
      sentence_len, is_extern_emb, token_emb_size, vocab_size
  ):
      if is_extern_emb:
          in_layer = e_layer = tf.keras.layers.Input(
              shape=(
                  sentence_len,
                  token_emb_size,
              ),
              dtype="float32",
              name="tokens_input",
          )
      else:
          in_layer = tf.keras.layers.Input(
              shape=(sentence_len,), dtype="int32", name="tokens_input"
          )
          e_layer = tf.keras.layers.Embedding(
              vocab_size,
              token_emb_size,
              input_length=sentence_len,
              name="embedding_layer",
          )(in_layer)
      return in_layer, e_layer
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  from nlp_architect.nn.tensorflow.python.keras.layers.crf import CRF
  from nlp_architect.nn.tensorflow.python.keras.utils import load_model, save_model
label_desc: Use image node_id 7 for calling the IntentExtractionModel obj's underlying member method code with example usage: obj._create_input_embed(sentence_len, is_extern_emb, token_emb_size, vocab_size) and returns: in_layer,  e_layer