Modernize transforms tutorial to torchvision v2 API (#3861)

Fixes #3853

## Description
files changed  - `beginner_source/basics/transforms_tutorial.py`

1. Replaced deprecated `torchvision.transforms.ToTensor` (deprecated
since torchvision 0.16) with the v2 pipeline `v2.Compose([v2.ToImage(),
v2.ToDtype(torch.float32, scale=True)])`.
2. Switched the legacy v1 `transforms` namespace import to `from
torchvision.transforms import v2`.
3. Replaced the low-level `torch.zeros(...).scatter_(...)` one-hot
pattern with `torch.nn.functional.one_hot(torch.tensor(y),
  num_classes=10).float()`, wrapped in `v2.Lambda`.
4. Updated the section heading and explanatory prose so they describe
`v2.ToImage()` + `v2.ToDtype()` and `F.one_hot` instead of `ToTensor`
and `scatter_`.

## Checklist
<!--- Make sure to add `x` to all items in the following checklist: -->
- [x] The issue that is being fixed is referred in the description (see
above "Fixes #ISSUE_NUMBER")
- [x] Only one issue is addressed in this pull request
- [ ] Labels from the issue that this PR is fixing are added to this
pull request
- [x] No unnecessary issues are included into this pull request.


cc @subramen

---------

Co-authored-by: sekyondaMeta <127536312+sekyondaMeta@users.noreply.github.com>

Author: omkar-334

beginner_source/basics/transforms_tutorial.py

@@ -23,42 +23,47 @@
 
 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
+import torch.nn.functional as F
 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: F.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: 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>`_