human
Complete this python program:
def _scal_elemwise_with_nfunc(nfunc, nin, nout):
assistant
#Here is the Python code you requested.

    """Replace a symbol definition with an elementwise version of the
    corresponding scalar Op.  If it is not None, the nfunc argument
    should be a string such that getattr(numpy, nfunc) implements
    a vectorized version of the elemwise operation. nin is the number
    of inputs expected by that function, and nout is the number of
    **destination** inputs it takes. That is, the function should
    take nin+nout inputs. nout == 0 means that the numpy function
    does not take a numpy array argument to put its result in.
    """
    def construct(symbol):
        symbolname = symbol.__name__
        inplace = symbolname.endswith('_inplace')
        if inplace:
            msg = 'inplace'
        else:
            msg = 'no_inplace'
        n = ('Elemwise{%s,%s}' % (symbolname, msg))
        if inplace:
            scalar_op = getattr(scal, symbolname[:(- len('_inplace'))])
            inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
            rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=n, nfunc_spec=(nfunc and (nfunc, nin, nout)))
        else:
            scalar_op = getattr(scal, symbolname)
            rval = elemwise.Elemwise(scalar_op, name=n, nfunc_spec=(nfunc and (nfunc, nin, nout)))
        if getattr(symbol, '__doc__', False):
            rval.__doc__ = ((symbol.__doc__ + '\n') + rval.__doc__)
        rval.__epydoc_asRoutine = symbol
        rval.__module__ = 'tensor'
        pprint.assign(rval, printing.FunctionPrinter(symbolname))
        return rval
    return construct