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.

aidge_core/unit_tests/test_recipies.py

@@ -45,9 +45,9 @@ class test_recipies(unittest.TestCase):
         self.assertTrue(all([i in old_nodes for i in graph_view.get_nodes()]))
 
     def test_fuse_matmul_add(self):
-        matmul0 = aidge_core.MatMul(1, 1, name="MatMul0")
+        matmul0 = aidge_core.MatMul(name="MatMul0")
         add0 = aidge_core.Add(2, name="Add0")
-        matmul1 = aidge_core.MatMul(1, 1, name="MatMul1")
+        matmul1 = aidge_core.MatMul(name="MatMul1")
         add1 = aidge_core.Add(2, name="Add1")
 
         graph_view = aidge_core.sequential([matmul0, add0, matmul1, add1])