Reuse executor for repeated prediction with different checkpoints

Testing the different checkpoints of a training run requires loading checkpoints and predictions runs in a tight loop.

``` julia
net = mx.load(archfile, mx.SymbolicNode)
arch = mx.FeedForward(net, context=mx.gpu())

data = ...

for wfile in weights
saved_dict = mx.load(wfile, mx.NDArray)
arg_params = Dict{Base.Symbol, mx.NDArray}()
aux_params = Dict{Base.Symbol, mx.NDArray}()
for (k,v) in saved_dict
tp, name = split(string(k), ':')
name = symbol(name)
if tp == "arg"
arg_params[name] = v
else
aux_params[name] = v
end
end

arch.arg_params = arg_params
arch.aux_params = aux_params

pred = mx.predict(arch, data)
# For memory reclaim, eagerly finalize self.pred_exec.handle
finalize(arch.pred_exec.handle)
arch.pred_exec=nothing
end
```

Without these final two lines this easily runs out of memory for big models/batch_sizes, because the executor is not gc'd yet and we are creating a new one asking for more memory.

If we could reuse the previous executor that problem would be alleviated.