human
Complete the below
def _getAdditionalSpecs(temporalImp, kwargs={}):

    ''''Build the additional specs in three groups (for the inspector)
    Use the type of the default argument to set the Spec type, defaulting
    to \'Byte\' for None and complex types
    Determines the spatial parameters based on the selected implementation.
    It defaults to TemporalMemory.
    Determines the temporal parameters based on the temporalImp'
    '''
assistant
#Here is the Python program you requested.
    typeNames = {int: 'UInt32', float: 'Real32', str: 'Byte', bool: 'bool', tuple: 'tuple'}
    def getArgType(arg):
        t = typeNames.get(type(arg), 'Byte')
        count = (0 if (t == 'Byte') else 1)
        if (t == 'tuple'):
            t = typeNames.get(type(arg[0]), 'Byte')
            count = len(arg)
        if (t == 'bool'):
            t = 'UInt32'
        return (t, count)
    def getConstraints(arg):
        t = typeNames.get(type(arg), 'Byte')
        if (t == 'Byte'):
            return 'multiple'
        elif (t == 'bool'):
            return 'bool'
        else:
            return ''
    TemporalClass = _getTPClass(temporalImp)
    tArgTuples = _buildArgs(TemporalClass.__init__)
    temporalSpec = {}
    for argTuple in tArgTuples:
        d = dict(description=argTuple[1], accessMode='ReadWrite', dataType=getArgType(argTuple[2])[0], count=getArgType(argTuple[2])[1], constraints=getConstraints(argTuple[2]))
        temporalSpec[argTuple[0]] = d
    temporalSpec.update(dict(columnCount=dict(description='Total    number    of    columns.', accessMode='Read', dataType='UInt32', count=1, constraints=''), cellsPerColumn=dict(description='Number    of    cells    per    column.', accessMode='Read', dataType='UInt32', count=1, constraints=''), inputWidth=dict(description='Number    of    inputs    to    the    TM.', accessMode='Read', dataType='UInt32', count=1, constraints=''), predictedSegmentDecrement=dict(description='Predicted    segment    decrement', accessMode='Read', dataType='Real', count=1, constraints=''), orColumnOutputs=dict(description='OR    together    the    cell    outputs    from    each    column    to    produce\n                        the    temporal    memory    output.    When    this    mode    is    enabled,    the    number    of\n                        cells    per    column    must    also    be    specified    and    the    output    size    of    the    region\n                        should    be    set    the    same    as    columnCount', accessMode='Read',...