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.

python_binding/operator/pybind_Matmul.cpp

@@ -22,7 +22,7 @@ namespace Aidge {
 void init_MatMul(py::module &m) {
   py::class_<MatMul_Op, std::shared_ptr<MatMul_Op>, OperatorTensor>(m, "MatMulOp", py::multiple_inheritance())
   .def("get_inputs_name", &MatMul_Op::getInputsName)
-  .def("get_outputs_name", &MatMul_Op::getOutputsName)
+  .def("get_outputs_name", &MatMul_Op::getOutputsName);
 
   m.def("MatMul", &MatMul, py::arg("name") = "");
 }