class_docstr: The model used as POS-tagger and chunker for sentence tagging, which contains three
  Bidirectional LSTM layers.
  
  This model can have one or two input(s):
  - word indices of shape (batch, sequence_length)
  *If char_vocab_size is not None:
  - character indices of shape (batch, sequence_length, word_length)
  This model has two outputs:
  - pos tags of shape (batch, sequence_length, num_pos_labels)
  - chunk tags of shape (batch, sequence_length, num_chunk_labels)
  
  :param num_pos_labels: Positive int. The number of pos labels to be classified.
  :param num_chunk_labels: Positive int. The number of chunk 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.
                          Default is None and in this case only one input, namely word indices
                          is expected.
  :param word_length: Positive int. The max word length in characters. Default is 12.
  :param feature_size: Positive int. The size of Embedding and Bi-LSTM layers. Default is 100.
  :param dropout: Dropout rate. Default is 0.5.
  :param classifier: String. The classification layer used for tagging chunks.
                     Either 'softmax' or 'crf' (Conditional Random Field). Default is 'softmax'.
  :param optimizer: Optimizer to train the model. If not specified, it will by default
                    to be tf.train.AdamOptimizer().
docstr: Load an existing SequenceTagger model (with weights) from HDF5 file.
  
  :param path: String. The path to the pre-defined model.
  :return: NER.
docstr_tok: ["Load","an","existing","SequenceTagger","model","(","with","weights",")","from","HDF5","file",".",":","param","path",":","String",".","The","path","to","the","pre-defined","model",".",":","return",":","NER","."]
code: def load_model(path):
      """
      Load an existing SequenceTagger model (with weights) from HDF5 file.
  
      :param path: String. The path to the pre-defined model.
      :return: NER.
      """
      labor = chunker.SequenceTagger(use_cudnn=False)
      model = TextKerasModel._load_model(labor, path)
      model.__class__ = SequenceTagger
      return model
  
imports: from nlp_architect.models import chunker
  from bigdl.orca.tfpark.text.keras.text_model import TextKerasModel
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 2 for calling the SequenceTagger obj's underlying member method code with example usage: obj.load_model(path) and returns: model