Matmul rework

[ADD] support for Multi-dimensional Matrix Multiplication, following numpy MatMul operation specificities.

In the context of Multi-dimensional MatMul, we consider an N-Dim Tensor as a stack of 2-D matrices having as shape the last two dimensions of the Tensor.

From Numpy documentation:

The behavior depends on the arguments in the following way.

• If both arguments are 2-D they are multiplied like conventional matrices.
• If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
• If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
• If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.

src/recipies/FuseMulAdd.cpp

@@ -41,7 +41,19 @@ void Aidge::fuseMulAdd(std::shared_ptr<Aidge::Node> matmulNode, std::shared_ptr<
     AIDGE_ASSERT(matmulNode->getParent(1), "No weight detected to produce the fuseMulAdd recipe.");
 
     std::shared_ptr<Node> weight = matmulNode->getParent(1)->cloneSharedOperators();
-    const DimSize_t outSize = std::dynamic_pointer_cast<MatMul_Op>(matmulNode->getOperator()) -> getAttr<DimSize_t>("OutChannels");
+    // TODO: find another way to get OutChannels for FC operator.
+    // This poor fix supposes that one of Add inputs is a const and has the same outChannels as the output
+    DimSize_t outSize = 0;
+    const auto& op = std::dynamic_pointer_cast<OperatorTensor>(addNode->getOperator());
+    for (size_t i = 0; i < op->nbInputs(); i++)
+    {
+        const auto& inTensor = op->getInput(i);
+        if(inTensor->nbDims() > 0) {
+            outSize = inTensor->dims()[inTensor->nbDims()-1];
+            break;
+        }
+    }
+    AIDGE_ASSERT(outSize, "Couldnt get output number of channels for FC operator.");
 
     // Instanciate FC
     //std::shared_ptr<Node> fc = FC(dim[0], false, "Fc");