Refactored with single dispatch generic function implementation (#19958)

* Refactored with single dispatch generic function implementation

* Resolved mypy linting warnings

* Update src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py

* Update src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py

---------

Co-authored-by: Przemyslaw Wysocki <przemyslaw.wysocki@intel.com>

src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py

@@ -10,6 +10,7 @@ import copy
 import numpy as np
 from typing import List, Dict
 from abc import ABCMeta, abstractmethod
+from functools import singledispatch
 from typing import Callable, Any, Union, Tuple
 from typing import Sequence as SequenceType
 from collections.abc import Sequence
@@ -46,15 +47,22 @@ TORCHTYPE_TO_OVTYPE = {
 }
 
 
+@singledispatch
 def _setup_size(size: Any, error_msg: str) -> SequenceType[int]:
-    # TODO: refactor into @singledispatch once Python 3.7 support is dropped
-    if isinstance(size, numbers.Number):
-        return int(size), int(size)  # type: ignore
-    if isinstance(size, Sequence):
-        if len(size) == 1:
-            return size[0], size[0]
-        elif len(size) == 2:
-            return size
+    raise ValueError(error_msg)
+
+
+@_setup_size.register
+def _setup_size_number(size: numbers.Number, error_msg: str) -> SequenceType[int]:
+    return int(size), int(size)  # type: ignore
+
+
+@_setup_size.register
+def _setup_size_sequence(size: Sequence, error_msg: str) -> SequenceType[int]:
+    if len(size) == 1:
+        return size[0], size[0]
+    elif len(size) == 2:
+        return size
     raise ValueError(error_msg)
 
 
@@ -66,14 +74,19 @@ def _NHWC_to_NCHW(input_shape: List) -> List:  # noqa N802
     return new_shape
 
 
+@singledispatch
 def _to_list(transform: Callable) -> List:
-    # TODO: refactor into @singledispatch once Python 3.7 support is dropped
-    if isinstance(transform, torch.nn.Sequential):
-        return list(transform)
-    elif isinstance(transform, transforms.Compose):
-        return transform.transforms
-    else:
-        raise TypeError(f"Unsupported transform type: {type(transform)}")
+    raise TypeError(f"Unsupported transform type: {type(transform)}")
+
+
+@_to_list.register
+def _to_list_torch_sequential(transform: torch.nn.Sequential) -> List:
+    return list(transform)
+
+
+@_to_list.register
+def _to_list_transforms_compose(transform: transforms.Compose) -> List:
+    return transform.transforms
 
 
 def _get_shape_layout_from_data(input_example: Union[torch.Tensor, np.ndarray, Image.Image]) -> Tuple[List, Layout]: