def make_node(self, *inputs):
        assert self.nout == 1
        assert len(inputs) == 2  # TODO remove
        _inputs = [gpu_contiguous(as_cuda_ndarray_variable(i)) for i in inputs]
        if self.nin > 0 and len(_inputs) != self.nin:
            raise TypeError('Wrong argument count', (self.nin, len(_inputs)))
        for i in _inputs[1:]:
            if i.type.ndim != inputs[0].type.ndim:
                raise TypeError('different ranks among inputs')

        if any([any(i.type.broadcastable) for i in inputs]):
            raise Exception("pycuda don't support broadcasted dimensions")

        otype = CudaNdarrayType(broadcastable=[False] * _inputs[0].type.ndim)
        out_node = Apply(self, _inputs, [otype() for o in xrange(self.nout)])
        return out_node
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/misc/pycuda_example.py/PycudaElemwiseSourceModuleMakeThunkOp.make_node/original