Prepare test_clone for cnn model

(using data_format metadata)

tests/conftest.py

@@ -1293,7 +1293,9 @@ def toy_struct_v2(
         return Model(x, y)
 
     @staticmethod
-    def toy_struct_cnn(input_shape: tuple[int, ...] = (6, 6, 2), dtype: Optional[str] = None):
+    def toy_struct_cnn(
+        input_shape: tuple[int, ...] = (6, 6, 2), dtype: Optional[str] = None, data_format="channels_last"
+    ):
         if dtype is None:
             dtype = keras_config.floatx()
         layers = [
@@ -1302,7 +1304,7 @@ def toy_struct_cnn(input_shape: tuple[int, ...] = (6, 6, 2), dtype: Optional[str
                 10,
                 kernel_size=(3, 3),
                 activation="relu",
-                data_format="channels_last",
+                data_format=data_format,
                 dtype=dtype,
             ),
             Flatten(dtype=dtype),

tests/test_clone.py

@@ -61,7 +61,11 @@ def test_clone(
     decimal = 4
 
     # keras model to convert
-    keras_model = toy_model_fn(input_shape=input_shape)
+    if toy_model_name == "cnn":
+        kwargs_toy_model = dict(data_format=model_decomon_input_metadata["data_format"])
+    else:
+        kwargs_toy_model = {}
+    keras_model = toy_model_fn(input_shape=input_shape, **kwargs_toy_model)
 
     # conversion
     decomon_model = clone(model=keras_model, slope=slope, perturbation_domain=perturbation_domain, method=method)