(model_name = "en-default.pyrnn")
return network, lnorm
This function load the neural network model from slapos backend
  and initialise it.
  ----------------------------
    @args:
      - model_name: string, default: en-default.pyrnn
          Id of the object in the filesystem that contain the rnn model
    @return:
      - network: lstm object
          Represent the trained neural net
      - lnorm: method from lstm object
          Represent the size of the lstm object. Is used to scale the objects
          to recognize from original size to the average network object.
  ----------------------------
  LSTM meaning: https://en.wikipedia.org/wiki/Long_short-term_memory
  lnorm is extracted for clarity. This function initialize the neural net after
  loading.
This function load the neural network model from slapos backend
  and initialise it.
  ----------------------------
This function load the neural network model from slapos backend and initialise it . ----------------------------
def initRnnModel(model_name = "en-default.pyrnn"):
  """
  This function load the neural network model from slapos backend
  and initialise it.
  ----------------------------
    @args:
      - model_name: string, default: en-default.pyrnn
          Id of the object in the filesystem that contain the rnn model
    @return:
      - network: lstm object
          Represent the trained neural net
      - lnorm: method from lstm object
          Represent the size of the lstm object. Is used to scale the objects
          to recognize from original size to the average network object.
  ----------------------------
  LSTM meaning: https://en.wikipedia.org/wiki/Long_short-term_memory
  lnorm is extracted for clarity. This function initialize the neural net after
  loading.
  """
  network = ocrolib.load_object(model_name)
  for node in network.walk():
    if isinstance(node, ocrolib.lstm.LSTM):
      node.allocate(5000)
  lnorm = getattr(network, "lnorm", None)

  return network, lnorm
def initRnnModel ( model_name = "en-default.pyrnn" ) : network = ocrolib . load_object ( model_name ) for node in network . walk ( ) : if isinstance ( node , ocrolib . lstm . LSTM ) : node . allocate ( 5000 ) lnorm = getattr ( network , "lnorm" , None ) return network , lnorm