Merge pull request #23843 from TolyaTalamanov:at/fix-missing-opaque-kind-for-kernel

G-API: Fix incorrect OpaqueKind for Kernel outputs #23843

### Pull Request Readiness Checklist

#### Overview
The PR is going to fix several problems:
1. Major: `GKernel` doesn't hold `kind` for its outputs. Since `GModelBuilder` traverse graph from outputs to inputs once it reaches any output of the operation it will use its `kind` to create  `Data` meta for all operation outputs. Since it essential for `python` to know `GTypeInfo` (which is `shape` and `kind`) it will be confused.

Consider this operation:
```
 @cv.gapi.op('custom.square_mean', in_types=[cv.GArray.Int], out_types=[cv.GOpaque.Float, cv.GArray.Int])
    class GSquareMean:
        @staticmethod
        def outMeta(desc):
            return cv.empty_gopaque_desc(), cv.empty_array_desc()
```
Even though `GOpaque` is `Float`, corresponding metadata might have `Int` kind because it might be taken from `cv.GArray.Int`
so it will be a problem if one of the outputs of these operation is graph output because python will cast it to the wrong type based on `Data` meta.

2. Minor: Some of the OpenVINO `IR`'s doesn't any layout information for input. It's usually true only for `IRv10` but since `OpenVINO 2.0` need this information to correctly configure resize we need to put default layout if there no such assigned in `ov::Model`. 

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

modules/gapi/misc/python/test/test_gapi_sample_pipelines.py

@@ -207,7 +207,48 @@ try:
         return Op
 
 
+    # NB: Just mock operation to test different kinds for output G-types.
+    @cv.gapi.op('custom.square_mean', in_types=[cv.GArray.Int], out_types=[cv.GOpaque.Float, cv.GArray.Int])
+    class GSquareMean:
+        @staticmethod
+        def outMeta(desc):
+            return cv.empty_gopaque_desc(), cv.empty_array_desc()
+
+
+    @cv.gapi.kernel(GSquareMean)
+    class GSquareMeanImpl:
+        @staticmethod
+        def run(arr):
+            squares = [val**2 for val in arr]
+            return sum(arr) / len(arr), squares
+
+    @cv.gapi.op('custom.squares', in_types=[cv.GArray.Int], out_types=[cv.GArray.Int])
+    class GSquare:
+        @staticmethod
+        def outMeta(desc):
+            return cv.empty_array_desc()
+
+
+    @cv.gapi.kernel(GSquare)
+    class GSquareImpl:
+        @staticmethod
+        def run(arr):
+            squares = [val**2 for val in arr]
+            return squares
+
+
     class gapi_sample_pipelines(NewOpenCVTests):
+        def test_different_output_opaque_kinds(self):
+            g_in = cv.GArray.Int()
+            g_mean, g_squares = GSquareMean.on(g_in)
+            comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_mean, g_squares))
+
+            pkg = cv.gapi.kernels(GSquareMeanImpl)
+            mean, squares = comp.apply(cv.gin([1,2,3]), args=cv.gapi.compile_args(pkg))
+
+            self.assertEqual([1,4,9], list(squares))
+            self.assertEqual(2.0, mean)
+
 
         def test_custom_op_add(self):
             sz = (3, 3)