diff --git a/gallery/plot_transforms_v2.py b/gallery/plot_transforms_v2.py
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+"""
+==================================
+Getting started with transforms v2
+==================================
+
+Most computer vision tasks are not supported out of the box by ``torchvision.transforms`` v1, since it only supports
+images. ``torchvision.transforms.v2`` enables jointly transforming images, videos, bounding boxes, and masks. This
+example showcases the core functionality of the new ``torchvision.transforms.v2`` API.
+"""
+
+import pathlib
+
+import torch
+import torchvision
+
+
+def load_data():
+    from torchvision.io import read_image
+    from torchvision import datapoints
+    from torchvision.ops import masks_to_boxes
+
+    assets_directory = pathlib.Path("assets")
+
+    path = assets_directory / "FudanPed00054.png"
+    image = datapoints.Image(read_image(str(path)))
+    merged_masks = read_image(str(assets_directory / "FudanPed00054_mask.png"))
+
+    labels = torch.unique(merged_masks)[1:]
+
+    masks = datapoints.Mask(merged_masks == labels.view(-1, 1, 1))
+
+    bounding_boxes = datapoints.BoundingBox(
+        masks_to_boxes(masks), format=datapoints.BoundingBoxFormat.XYXY, spatial_size=image.shape[-2:]
+    )
+
+    return path, image, bounding_boxes, masks, labels
+
+
+########################################################################################################################
+# The :mod:`torchvision.transforms.v2` API supports images, videos, bounding boxes, and instance and segmentation
+# masks. Thus, it offers native support for many Computer Vision tasks, like image and video classification, object
+# detection or instance and semantic segmentation. Still, the interface is the same, making
+# :mod:`torchvision.transforms.v2` a drop-in replacement for the existing :mod:`torchvision.transforms` API, aka v1.
+
+# 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()
+import torchvision.transforms.v2 as transforms
+
+transform = transforms.Compose(
+    [
+        transforms.ColorJitter(contrast=0.5),
+        transforms.RandomRotation(30),
+        transforms.CenterCrop(480),
+    ]
+)
+
+########################################################################################################################
+# :mod:`torchvision.transforms.v2` natively supports jointly transforming multiple inputs while making sure that
+# potential random behavior is consistent across all inputs. However, it doesn't enforce a specific input structure or
+# order.
+
+path, image, bounding_boxes, masks, labels = load_data()
+
+torch.manual_seed(0)
+new_image = transform(image)  # Image Classification
+new_image, new_bounding_boxes, new_labels = transform(image, bounding_boxes, labels)  # Object Detection
+new_image, new_bounding_boxes, new_masks, new_labels = transform(
+    image, bounding_boxes, masks, labels
+)  # Instance Segmentation
+new_image, new_target = transform((image, {"boxes": bounding_boxes, "labels": labels}))  # Arbitrary Structure
+
+########################################################################################################################
+# Under the hood, :mod:`torchvision.transforms.v2` relies on :mod:`torchvision.datapoints` for the dispatch to the
+# appropriate function for the input data: :ref:`sphx_glr_auto_examples_plot_datapoints.py`. Note however, that as
+# regular user, you likely don't have to touch this yourself. See
+# :ref:`sphx_glr_auto_examples_plot_transforms_v2_e2e.py`.
+#
+# All "foreign" types like :class:`str`'s or :class:`pathlib.Path`'s are passed through, allowing to store extra
+# information directly with the sample:
+
+sample = {"path": path, "image": image}
+new_sample = transform(sample)
+
+assert new_sample["path"] is sample["path"]
+
+########################################################################################################################
+# As stated above, :mod:`torchvision.transforms.v2` is a drop-in replacement for :mod:`torchvision.transforms` and thus
+# also supports transforming plain :class:`torch.Tensor`'s as image or video if applicable. This is achieved with a
+# simple heuristic:
+#
+# * If we find an explicit image or video (:class:`torchvision.datapoints.Image`, :class:`torchvision.datapoints.Video`,
+#   or :class:`PIL.Image.Image`) in the input, all other plain tensors are passed through.
+# * If there is no explicit image or video, only the first plain :class:`torch.Tensor` will be transformed as image or
+#   video, while all others will be passed through.
+
+plain_tensor_image = torch.rand(image.shape)
+
+print(image.shape, plain_tensor_image.shape)
+
+# passing a plain tensor together with an explicit image, will not transform the former
+plain_tensor_image, image = transform(plain_tensor_image, image)
+
+print(image.shape, plain_tensor_image.shape)
+
+# passing a plain tensor without an explicit image, will transform the former
+plain_tensor_image, _ = transform(plain_tensor_image, bounding_boxes)
+
+print(image.shape, plain_tensor_image.shape)