parameters: (model_name = "en-default.pyrnn")
return_statement: return network, lnorm
docstring: 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.
docstring_summary: This function load the neural network model from slapos backend
    and initialise it.
    ----------------------------
docstring_tokens: This function load the neural network model from slapos backend and initialise it . ----------------------------
function: 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
function_tokens: 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