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_parameters.py

@@ -39,12 +39,6 @@ class test_attributes(unittest.TestCase):
         self.assertEqual(fc_op.get_attr("OutChannels"), out_channels)
         self.assertEqual(fc_op.get_attr("NoBias"), nb_bias)
 
-    def test_matmul(self):
-        in_channels = 4
-        out_channels = 8
-        matmul_op = aidge_core.MatMul(in_channels, out_channels).get_operator()
-        self.assertEqual(matmul_op.get_attr("OutChannels"), out_channels)
-
     def test_producer_1D(self):
         dims = [5]
         producer_op = aidge_core.Producer(dims).get_operator()