diff --git a/gallery/plot_datapoints.py b/gallery/plot_datapoints.py
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+"""
+==============
+Datapoints FAQ
+==============
+
+The :mod:`torchvision.datapoints` namespace was introduced together with ``torchvision.transforms.v2``. This example
+showcases what these datapoints are and how they behave. This is a fairly low-level topic that most users will not need
+to worry about: you do not need to understand the internals of datapoints to efficiently rely on
+``torchvision.transforms.v2``. It may however be useful for advanced users trying to implement their own datasets,
+transforms, or work directly with the datapoints.
+"""
+
+import PIL.Image
+
+import torch
+import torchvision
+
+# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
+# some APIs may slightly change in the future
+torchvision.disable_beta_transforms_warning()
+
+from torchvision import datapoints
+
+
+########################################################################################################################
+# What are datapoints?
+# --------------------
+#
+# Datapoints are zero-copy tensor subclasses:
+
+tensor = torch.rand(3, 256, 256)
+image = datapoints.Image(tensor)
+
+assert isinstance(image, torch.Tensor)
+assert image.data_ptr() == tensor.data_ptr()
+
+
+########################################################################################################################
+# Under the hood, they are needed in :mod:`torchvision.transforms.v2` to correctly dispatch to the appropriate function
+# for the input data.
+#
+# What datapoints are supported?
+# ------------------------------
+#
+# So far :mod:`torchvision.datapoints` supports four types of datapoints:
+#
+# * :class:`~torchvision.datapoints.Image`
+# * :class:`~torchvision.datapoints.Video`
+# * :class:`~torchvision.datapoints.BoundingBox`
+# * :class:`~torchvision.datapoints.Mask`
+#
+# How do I construct a datapoint?
+# -------------------------------
+#
+# Each datapoint class takes any tensor-like data that can be turned into a :class:`~torch.Tensor`
+
+image = datapoints.Image([[[[0, 1], [1, 0]]]])
+print(image)
+
+
+########################################################################################################################
+# Similar to other PyTorch creations ops, the constructor also takes the ``dtype``, ``device``, and ``requires_grad``
+# parameters.
+
+float_image = datapoints.Image([[[0, 1], [1, 0]]], dtype=torch.float32, requires_grad=True)
+print(float_image)
+
+
+########################################################################################################################
+# In addition, :class:`~torchvision.datapoints.Image` and :class:`~torchvision.datapoints.Mask` also take a
+# :class:`PIL.Image.Image` directly:
+
+image = datapoints.Image(PIL.Image.open("assets/astronaut.jpg"))
+print(image.shape, image.dtype)
+
+########################################################################################################################
+# In general, the datapoints can also store additional metadata that complements the underlying tensor. For example,
+# :class:`~torchvision.datapoints.BoundingBox` stores the coordinate format as well as the spatial size of the
+# corresponding image alongside the actual values:
+
+bounding_box = datapoints.BoundingBox(
+    [17, 16, 344, 495], format=datapoints.BoundingBoxFormat.XYXY, spatial_size=image.shape[-2:]
+)
+print(bounding_box)
+
+
+########################################################################################################################
+# Do I have to wrap the output of the datasets myself?
+# ----------------------------------------------------
+#
+# Only if you are using custom datasets. For the built-in ones, you can use
+# :func:`torchvision.datasets.wrap_dataset_for_transforms_v2`. Note that the function also supports subclasses of the
+# built-in datasets. Meaning, if your custom dataset subclasses from a built-in one and the output type is the same, you
+# also don't have to wrap manually.
+#
+# How do the datapoints behave inside a computation?
+# --------------------------------------------------
+#
+# Datapoints look and feel just like regular tensors. Everything that is supported on a plain :class:`torch.Tensor`
+# also works on datapoints.
+# Since for most operations involving datapoints, it cannot be safely inferred whether the result should retain the
+# datapoint type, we choose to return a plain tensor instead of a datapoint (this might change, see note below):
+
+assert isinstance(image, datapoints.Image)
+
+new_image = image + 0
+
+assert isinstance(new_image, torch.Tensor) and not isinstance(new_image, datapoints.Image)
+
+########################################################################################################################
+# .. note::
+#
+#    This "unwrapping" behaviour is something we're actively seeking feedback on. If you find this surprising or if you
+#    have any suggestions on how to better support your use-cases, please reach out to us via this issue:
+#    https://github.com/pytorch/vision/issues/7319
+#
+# There are two exceptions to this rule:
+#
+# 1. The operations :meth:`~torch.Tensor.clone`, :meth:`~torch.Tensor.to`, and :meth:`~torch.Tensor.requires_grad_`
+#    retain the datapoint type.
+# 2. Inplace operations on datapoints cannot change the type of the datapoint they are called on. However, if you use
+#    the flow style, the returned value will be unwrapped:
+
+image = datapoints.Image([[[0, 1], [1, 0]]])
+
+new_image = image.add_(1).mul_(2)
+
+assert isinstance(image, torch.Tensor)
+print(image)
+
+assert isinstance(new_image, torch.Tensor) and not isinstance(new_image, datapoints.Image)
+assert (new_image == image).all()