def isTemporal(inferenceElement):
    """ Returns True if the inference from this timestep is predicted the input
    for the NEXT timestep.

    NOTE: This should only be checked IF THE MODEL'S INFERENCE TYPE IS ALSO
    TEMPORAL. That is, a temporal model CAN have non-temporal inference elements,
    but a non-temporal model CANNOT have temporal inference elements
    """
    if InferenceElement.__temporalInferenceElements is None:
      InferenceElement.__temporalInferenceElements = \
                                set([InferenceElement.prediction])

    return inferenceElement in InferenceElement.__temporalInferenceElements
def isTemporal(inferenceElement):
    """ Returns True if the inference from this timestep is predicted the input
    for the NEXT timestep.

    NOTE: This should only be checked IF THE MODEL'S INFERENCE TYPE IS ALSO
    TEMPORAL. That is, a temporal model CAN have non-temporal inference elements,
    but a non-temporal model CANNOT have temporal inference elements
    """
    if InferenceElement.__temporalInferenceElements is None:
      InferenceElement.__temporalInferenceElements = \
                                set([InferenceElement.prediction])

    return inferenceElement in InferenceElement.__temporalInferenceElements
def isTemporal ( inferenceElement ) : if InferenceElement . __temporalInferenceElements is None : InferenceElement . __temporalInferenceElements = set ( [ InferenceElement . prediction ] ) return inferenceElement in InferenceElement . __temporalInferenceElements
Returns True if the inference from this timestep is predicted the input
    for the NEXT timestep.

    NOTE: This should only be checked IF THE MODEL'S INFERENCE TYPE IS ALSO
    TEMPORAL. That is, a temporal model CAN have non-temporal inference elements,
    but a non-temporal model CANNOT have temporal inference elements
Returns True if the inference from this timestep is predicted the input for the NEXT timestep .