Multiple fixes to enable multi-GPUs forward execution

Some of these fix allow multi-GPU forward execution of cloned graphs.

• Correctly escape HTML special characters in GraphView::save();
• Added const version of refCastFrom();
• Make GraphView::add() from another GraphView preserve ordered inputs/outputs;
• Operators attributes were not properly cloned (should fix aidge!105 (merged)).

This now works:

aidge_model0 = aidge_onnx.load_onnx("mymodel.onnx")
aidge_model1 = aidge_model0.clone()
aidge_model0.set_backend("cuda", 0)
aidge_model1.set_backend("cuda", 1)

aidge_models = aidge_core.GraphView()
aidge_models.add(aidge_model0)
aidge_models.add(aidge_model1)

scheduler = aidge_core.ParallelScheduler(aidge_models)
scheduler.forward(True, [in_model0, in_model1])

The models aidge_model0 and aidge_model1 will be run in parallel on 2 GPUs, yet in a synchronized fashion (the same layers are executed at the same time). This should be generalizable to any number of device/backend combinations!

This also works:

aidge_model0 = aidge_onnx.load_onnx("mymodel.onnx")
aidge_model1 = aidge_model0.clone()
aidge_model0.set_backend("cuda", 0)
aidge_model1.set_backend("cuda", 1)

scheduler0 = aidge_core.SequentialScheduler(aidge_model0)
scheduler1 = aidge_core.SequentialScheduler(aidge_model1)

import concurrent.futures

with concurrent.futures.ThreadPoolExecutor(max_workers=2) as e:
    e.submit(scheduler0.forward, True, [in_model0])
    e.submit(scheduler1.forward, True, [in_model1])

Here models aidge_model0 and aidge_model1 are run in parallel fully asynchronously.

include/aidge/data/Tensor.hpp

@@ -885,6 +885,10 @@ public:
         // First refFrom, to ensure that fallback, if required, is also on desired device
         return refFrom(fallback, backend, device).refCast(fallback, dt);
     }
+    const Tensor& refCastFrom(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt, const std::string &backend, DeviceIdx_t device = 0) const {
+        // First refFrom, to ensure that fallback, if required, is also on desired device
+        return refFrom(fallback, backend, device).refCast(fallback, dt);
+    }
 
     /**
      * Return a reference to a Tensor with same characteristics
@@ -904,6 +908,10 @@ public:
         const auto& device = targetReqs.getImpl()->device();
         return refCastFrom(fallback, targetReqs.dataType(), device.first, device.second);
     }
+    const Tensor& refCastFrom(std::shared_ptr<Tensor>& fallback, const Tensor& targetReqs) const {
+        const auto& device = targetReqs.getImpl()->device();
+        return refCastFrom(fallback, targetReqs.dataType(), device.first, device.second);
+    }
 
     /**
      * @brief Return a reference to a Tensor on desired data type and backend/device:
@@ -941,6 +949,10 @@ public:
         const auto& device = targetReqs.getImpl()->device();
         return ref(fallback, targetReqs.dataType(), device.first, device.second);
     }
+    const Tensor& ref(std::shared_ptr<Tensor>& fallback, const Tensor& targetReqs) const {
+        const auto& device = targetReqs.getImpl()->device();
+        return ref(fallback, targetReqs.dataType(), device.first, device.second);
+    }
 
 
     /**