feat: add convolution extension

extend the current tracing and compilation with convolution, which
should compile to the FHELinalg.conv2d operation from the compiler

tests/numpy/test_compile_conv.py

@@ -0,0 +1,44 @@
+"""Test module for convolution compilation and execution."""
+
+import numpy as np
+import pytest
+
+import concrete.numpy as hnp
+from concrete.common.data_types.integers import Integer
+from concrete.common.values.tensors import EncryptedTensor
+from concrete.numpy.compile import compile_numpy_function
+
+
+@pytest.mark.parametrize(
+    "input_shape, weight_shape",
+    [
+        pytest.param((1, 1, 4, 4), (1, 1, 2, 2)),
+        pytest.param((4, 3, 4, 4), (2, 3, 2, 2)),
+    ],
+)
+@pytest.mark.parametrize("strides", [(2, 2)])
+@pytest.mark.parametrize("dilations", [(1, 1)])
+@pytest.mark.parametrize("has_bias", [True, False])
+def test_compile_and_run(
+    input_shape, weight_shape, strides, dilations, has_bias, default_compilation_configuration
+):
+    """Test function to make sure compilation and execution of conv2d works properly"""
+    if has_bias:
+        bias = np.random.randint(0, 4, size=(weight_shape[0],))
+    else:
+        bias = None
+    weight = np.random.randint(0, 4, size=weight_shape)
+
+    def conv(x):
+        return hnp.conv2d(x, weight, bias, strides=strides, dilations=dilations)
+
+    compiler_engine = compile_numpy_function(
+        conv,
+        {"x": EncryptedTensor(Integer(64, False), input_shape)},
+        [np.random.randint(0, 4, size=input_shape) for i in range(20)],
+        default_compilation_configuration,
+    )
+    x = np.random.randint(0, 4, size=input_shape, dtype=np.uint8)
+    expected = conv(x)
+    result = compiler_engine.run(x)
+    assert (expected == result).all()