diff --git a/torchvision/prototype/datapoints/__init__.py b/torchvision/prototype/datapoints/__init__.py
index 92f345e20bd..fa36302e633 100644
--- a/torchvision/prototype/datapoints/__init__.py
+++ b/torchvision/prototype/datapoints/__init__.py
@@ -1,5 +1,6 @@
 from ._bounding_box import BoundingBox, BoundingBoxFormat
 from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT
+from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT  # TODO: these may not need to be public?
 from ._image import ColorSpace, Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
 from ._label import Label, OneHotLabel
 from ._mask import Mask
diff --git a/torchvision/prototype/datapoints/_bounding_box.py b/torchvision/prototype/datapoints/_bounding_box.py
index 398770cbf6a..cc5cfe88fb4 100644
--- a/torchvision/prototype/datapoints/_bounding_box.py
+++ b/torchvision/prototype/datapoints/_bounding_box.py
@@ -16,8 +16,8 @@ class BoundingBoxFormat(StrEnum):
 
 
 class BoundingBox(Datapoint):
-    format: BoundingBoxFormat
-    spatial_size: Tuple[int, int]
+    format: BoundingBoxFormat  # TODO: do not use a builtin?
+    spatial_size: Tuple[int, int]  # TODO: This is the size of the image, not the box. Should we make the name more obvious?
 
     @classmethod
     def _wrap(cls, tensor: torch.Tensor, *, format: BoundingBoxFormat, spatial_size: Tuple[int, int]) -> BoundingBox:
diff --git a/torchvision/prototype/datapoints/_datapoint.py b/torchvision/prototype/datapoints/_datapoint.py
index 659d4e958cc..b5009a9c354 100644
--- a/torchvision/prototype/datapoints/_datapoint.py
+++ b/torchvision/prototype/datapoints/_datapoint.py
@@ -15,6 +15,8 @@
 FillTypeJIT = Union[int, float, List[float], None]
 
 
+# TODO: provide a few examples of when the Datapoint type is preserved vs when it's not
+# test_prototype_datapoints.py is a good starting point
 class Datapoint(torch.Tensor):
     __F: Optional[ModuleType] = None
 
@@ -89,6 +91,7 @@ def __torch_function__(
 
         with DisableTorchFunctionSubclass():
             output = func(*args, **kwargs or dict())
+            # TODO: maybe we can exit the CM here?
 
             wrapper = cls._NO_WRAPPING_EXCEPTIONS.get(func)
             # Apart from `func` needing to be an exception, we also require the primary operand, i.e. `args[0]`, to be
@@ -98,8 +101,14 @@ def __torch_function__(
             # `args = (torch.Tensor(), datapoints.Image())` first. Without this guard, the original `torch.Tensor` would
             # be wrapped into a `datapoints.Image`.
             if wrapper and isinstance(args[0], cls):
+                # TODO: figure out whether
+                # arbitrary_tensor.to(some_img)
+                # should be an Image or a Tensor
                 return wrapper(cls, args[0], output)  # type: ignore[no-any-return]
 
+            # Does that mean that DisableTorchFunctionSubclass is ignored for `.inpace_()` functions?
+            # TODO: figure out with torch core whether this is a bug or not
+
             # Inplace `func`'s, canonically identified with a trailing underscore in their name like `.add_(...)`,
             # will retain the input type. Thus, we need to unwrap here.
             if isinstance(output, cls):
diff --git a/torchvision/prototype/datapoints/_image.py b/torchvision/prototype/datapoints/_image.py
index fc20691100f..971e606ea43 100644
--- a/torchvision/prototype/datapoints/_image.py
+++ b/torchvision/prototype/datapoints/_image.py
@@ -58,6 +58,8 @@ def _from_tensor_shape(shape: List[int]) -> ColorSpace:
 
 
 class Image(Datapoint):
+    # For now, this is somewhat redundant with number of channels.
+    # TODO: decide whether we want to keep it?
     color_space: ColorSpace
 
     @classmethod
diff --git a/torchvision/prototype/transforms/_transform.py b/torchvision/prototype/transforms/_transform.py
index 43224cabd38..ff00b023bdc 100644
--- a/torchvision/prototype/transforms/_transform.py
+++ b/torchvision/prototype/transforms/_transform.py
@@ -35,6 +35,9 @@ def forward(self, *inputs: Any) -> Any:
 
         params = self._get_params(flat_inputs)
 
+        # TODO: right now, any tensor or datapoint passed to forward() will be transformed.
+        # The rest is bypassed.
+        # What if there are tensor parameters that users don't want to be transformed? Is that a plausible scenario?
         flat_outputs = [
             self._transform(inpt, params) if check_type(inpt, self._transformed_types) else inpt for inpt in flat_inputs
         ]
diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index ba417a0ce84..75dea0f3f60 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -244,6 +244,8 @@ def resize(
     if isinstance(inpt, torch.Tensor) and (
         torch.jit.is_scripting() or not isinstance(inpt, datapoints._datapoint.Datapoint)
     ):
+    # TODO: Figure out whether this cond could just be:
+    # if torch.jit.is_scripting() or is_simple_tensor(inpt):
         return resize_image_tensor(inpt, size, interpolation=interpolation, max_size=max_size, antialias=antialias)
     elif isinstance(inpt, datapoints._datapoint.Datapoint):
         return inpt.resize(size, interpolation=interpolation, max_size=max_size, antialias=antialias)