From 90cde13c97393312a03d0b877aa709a77cc5d30d Mon Sep 17 00:00:00 2001
From: Omkar Kabde <omkarkabde@gmail.com>
Date: Tue, 5 May 2026 23:01:43 +0530
Subject: [PATCH 1/2] Modernize transforms tutorial to torchvision v2 API

---
 beginner_source/basics/transforms_tutorial.py | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/beginner_source/basics/transforms_tutorial.py b/beginner_source/basics/transforms_tutorial.py
index 33076958bf5..320137b26e8 100644
--- a/beginner_source/basics/transforms_tutorial.py
+++ b/beginner_source/basics/transforms_tutorial.py
@@ -23,42 +23,42 @@
 
 The FashionMNIST features are in PIL Image format, and the labels are integers.
 For training, we need the features as normalized tensors, and the labels as one-hot encoded tensors.
-To make these transformations, we use ``ToTensor`` and ``Lambda``.
+To make these transformations, we use the ``torchvision.transforms.v2`` API along with ``torch.nn.functional.one_hot``.
 """
 
 import torch
 from torchvision import datasets
-from torchvision.transforms import ToTensor, Lambda
+from torchvision.transforms import v2
 
 ds = datasets.FashionMNIST(
     root="data",
     train=True,
     download=True,
-    transform=ToTensor(),
-    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
+    target_transform=v2.Lambda(lambda y: torch.nn.functional.one_hot(torch.tensor(y), num_classes=10).float())
 )
 
 #################################################
-# ToTensor()
+# ToImage() and ToDtype()
 # -------------------------------
 #
-# `ToTensor <https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor>`_
-# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor``. and scales
-# the image's pixel intensity values in the range [0., 1.]
+# The ``torchvision.transforms.v2`` API replaces the legacy ``ToTensor`` transform with a two-step pipeline.
+# `v2.ToImage <https://pytorch.org/vision/stable/generated/torchvision.transforms.v2.ToImage.html>`_
+# converts a PIL image or NumPy ``ndarray`` into a ``torchvision.tv_tensors.Image`` tensor, and
+# `v2.ToDtype <https://pytorch.org/vision/stable/generated/torchvision.transforms.v2.ToDtype.html>`_
+# with ``scale=True`` casts it to ``float32`` and scales the pixel intensity values to the range [0., 1.].
 #
 
 ##############################################
 # Lambda Transforms
 # -------------------------------
 #
-# Lambda transforms apply any user-defined lambda function. Here, we define a function
-# to turn the integer into a one-hot encoded tensor.
-# It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls
-# `scatter_ <https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html>`_ which assigns a
-# ``value=1`` on the index as given by the label ``y``.
+# Lambda transforms apply any user-defined lambda function. Here, we use
+# `torch.nn.functional.one_hot <https://pytorch.org/docs/stable/generated/torch.nn.functional.one_hot.html>`_
+# to turn the integer label into a one-hot encoded tensor of size 10 (the number of labels in our dataset),
+# then cast it to ``float`` to match the expected dtype.
 
-target_transform = Lambda(lambda y: torch.zeros(
-    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))
+target_transform = v2.Lambda(lambda y: torch.nn.functional.one_hot(torch.tensor(y), num_classes=10).float())
 
 ######################################################################
 # --------------

From a2ec6656dd29ad90f2040dc2feec454b84236855 Mon Sep 17 00:00:00 2001
From: Omkar Kabde <omkarkabde@gmail.com>
Date: Wed, 6 May 2026 20:26:48 +0530
Subject: [PATCH 2/2] use `F` instead of full name and update docs

---
 beginner_source/basics/transforms_tutorial.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/beginner_source/basics/transforms_tutorial.py b/beginner_source/basics/transforms_tutorial.py
index 320137b26e8..03ebbeee737 100644
--- a/beginner_source/basics/transforms_tutorial.py
+++ b/beginner_source/basics/transforms_tutorial.py
@@ -27,6 +27,7 @@
 """
 
 import torch
+import torch.nn.functional as F
 from torchvision import datasets
 from torchvision.transforms import v2
 
@@ -35,7 +36,9 @@
     train=True,
     download=True,
     transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
-    target_transform=v2.Lambda(lambda y: torch.nn.functional.one_hot(torch.tensor(y), num_classes=10).float())
+    target_transform=v2.Lambda(
+        lambda y: F.one_hot(torch.tensor(y), num_classes=10).float()
+    ),
 )
 
 #################################################
@@ -58,7 +61,9 @@
 # to turn the integer label into a one-hot encoded tensor of size 10 (the number of labels in our dataset),
 # then cast it to ``float`` to match the expected dtype.
 
-target_transform = v2.Lambda(lambda y: torch.nn.functional.one_hot(torch.tensor(y), num_classes=10).float())
+target_transform = v2.Lambda(
+    lambda y: F.one_hot(torch.tensor(y), num_classes=10).float()
+)
 
 ######################################################################
 # --------------
@@ -67,4 +72,5 @@
 #################################################################
 # Further Reading
 # ~~~~~~~~~~~~~~~~~
-# - `torchvision.transforms API <https://pytorch.org/vision/stable/transforms.html>`_
+# - `Getting started with transforms v2 <https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_getting_started.html>`_
+# - `torchvision.transforms.v2 API <https://pytorch.org/vision/stable/transforms.html#v2-api-reference-recommended>`_