Support RandTorchVisiond as RandomizableTransform (#5567)

Signed-off-by: Sachidanand Alle <sachidanand.alle@gmail.com>

Fixes # .
Instead of simple Randomizable transform, extend RandTorchVisiond into
RandomizableTransform

### Description
This will help users to choose to apply the transform with some
probability. For example, we want to mix training samples both with and
without ColorJitter (as during validation we don't use ColorJitter)


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Sachidanand Alle <sachidanand.alle@gmail.com>

Author: SachidanandAlle

monai/transforms/utility/dictionary.py

@@ -1405,7 +1405,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
         return d
 
 
-class RandTorchVisiond(Randomizable, MapTransform):
+class RandTorchVisiond(RandomizableTransform, MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.TorchVision` for randomized transforms.
     For deterministic non-randomized transforms of TorchVision use :py:class:`monai.transforms.TorchVisiond`.
@@ -1414,32 +1414,42 @@ class RandTorchVisiond(Randomizable, MapTransform):
 
         - As most of the TorchVision transforms only work for PIL image and PyTorch Tensor, this transform expects input
           data to be dict of PyTorch Tensors, users can easily call `ToTensord` transform to convert Numpy to Tensor.
-        - This class inherits the ``Randomizable`` purely to prevent any dataset caching to skip the transform
+        - This class inherits the ``RandomizableTransform`` purely to prevent any dataset caching to skip the transform
           computation. If the random factor of the underlying torchvision transform is not derived from `self.R`,
-          the results may not be deterministic.
-          See Also: :py:class:`monai.transforms.Randomizable`.
+          the results may not be deterministic.  It also provides the probability to apply this transform.
+          See Also: :py:class:`monai.transforms.RandomizableTransform`.
 
     """
 
     backend = TorchVision.backend
 
-    def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:
+    def __init__(
+        self, keys: KeysCollection, name: str, prob: float = 1.0, allow_missing_keys: bool = False, *args, **kwargs
+    ) -> None:
         """
         Args:
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
             name: The transform name in TorchVision package.
+            prob: Probability of applying this transform.
             allow_missing_keys: don't raise exception if key is missing.
             args: parameters for the TorchVision transform.
             kwargs: parameters for the TorchVision transform.
 
         """
+        RandomizableTransform.__init__(self, prob=prob)
         MapTransform.__init__(self, keys, allow_missing_keys)
+
         self.name = name
         self.trans = TorchVision(name, *args, **kwargs)
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
         d = dict(data)
+
+        self.randomize(data)
+        if not self._do_transform:
+            return d
+
         for key in self.key_iterator(d):
             d[key] = self.trans(d[key])
         return d