Fix Transforms tutorial: unify target_transform and ToTensor prose

beginner_source/basics/transforms_tutorial.py

@@ -30,35 +30,38 @@
 from torchvision import datasets
 from torchvision.transforms import ToTensor, Lambda
 
+target_transform = Lambda(
+    lambda y: torch.zeros(10, dtype=torch.float32).scatter_(
+        dim=0, index=torch.tensor(y), value=1
+    )
+)
+
 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))
+    target_transform=target_transform,
 )
 
 #################################################
 # ToTensor()
 # -------------------------------
 #
 # `ToTensor <https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor>`_
-# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor``. and scales
+# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor`` and scales
 # the image's pixel intensity values in 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``.
-
-target_transform = Lambda(lambda y: torch.zeros(
-    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))
+# Lambda transforms apply any user-defined callable. Above, ``target_transform`` wraps a
+# small lambda that turns each label ``y`` into a one-hot encoded tensor: it allocates a
+# length-10 zero vector and uses
+# `scatter_ <https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html>`_ to write
+# ``value=1`` at index ``y``.
 
 ######################################################################
 # --------------