class_docstr: A multi-task model used for joint intent extraction and slot filling.
  
  This model has two inputs:
  - word indices of shape (batch, sequence_length)
  - character indices of shape (batch, sequence_length, word_length)
  This model has two outputs:
  - intent labels of shape (batch, num_intents)
  - entity tags of shape (batch, sequence_length, num_entities)
  
  :param num_intents: Positive int. The number of intent classes to be classified.
  :param num_entities: Positive int. The number of slot labels to be classified.
  :param word_vocab_size: Positive int. The size of the word dictionary.
  :param char_vocab_size: Positive int. The size of the character dictionary.
  :param word_length: Positive int. The max word length in characters. Default is 12.
  :param word_emb_dim: Positive int. The dimension of word embeddings. Default is 100.
  :param char_emb_dim: Positive int. The dimension of character embeddings. Default is 30.
  :param char_lstm_dim: Positive int. The hidden size of character feature Bi-LSTM layer.
                        Default is 30.
  :param tagger_lstm_dim: Positive int. The hidden size of tagger Bi-LSTM layers. Default is 100.
  :param dropout: Dropout rate. Default is 0.2.
  :param optimizer: Optimizer to train the model.
                    If not specified, it will by default to be tf.train.AdamOptimizer().
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docstr: Load an existing IntentEntity model (with weights) from HDF5 file.
  
  :param path: String. The path to the pre-defined model.
  :return: IntentEntity.
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code: def load_model(path):
      """
      Load an existing IntentEntity model (with weights) from HDF5 file.
  
      :param path: String. The path to the pre-defined model.
      :return: IntentEntity.
      """
      labor = intent_models.MultiTaskIntentModel(use_cudnn=False)
      model = TextKerasModel._load_model(labor, path)
      model.__class__ = IntentEntity
      return model
  
imports: import nlp_architect.models.intent_extraction
  from bigdl.orca.tfpark.text.keras.text_model import TextKerasModel
label_desc: Use image node_id 2 for calling the IntentEntity obj's underlying member method code with example usage: obj.load_model(path) and returns: model