I'm not sure why, and I don't have sufficient python knowledge to figure
out. If anyone wants to give it a try please go ahead.

The just point to the existing implementation.

I'll try to add a test for it soon

In DebugMode, preemptive modif in case some op check if they have been applied in the Compute_map when their thunk is executed.

This optimization has been moved in sandbox/cuda

I've added another version of the optimization for gpu scans.

This import used to be on top of the file, but that creates a cycling
import.

preparing for the cython code, I used to grab the wrong inputs ( it should
be the node inputs not self.inputs)

reconstruct_graph always replaces CudaNdarrays with TensorTypes, so in case
of a gpu scan op, we can not use that logic to generate a hash ( I compute
it locally when I am generating the gpu scan op).

The way it works, __init__ gets a lambda function that construct a Tensor
Type. By default constructs Tensor Types, but move to gpu will replace it
with a CudaNdarray constructor.

Notes on this change: 1) I needed to replicate the inplace optimization, to
make a version that can handle ops moved on the gpu. I've also added a
replica of the reconstruct_graph function from scan_utils that again does
not replace CudaNdarray with TensorTypes ..