Fix typo "handles" to "handled" in pinmem_nonblock.py (#3807)

champion254 · danko · sekyondaMeta · web-flow · commit 084b358825cd · 2026-04-01T09:33:25.000-04:00
## Description
This PR fixes a small grammatical typo in `pinmem_nonblock.py`.

- **Original:** "...cuda streams can be handles using"
- **Fixed:** "...cuda streams can be handled using"

## Checklist
&lt;!--- Make sure to add `x` to all items in the following checklist: --&gt;
- [x] The issue that is being fixed is referred in the description (see
above "Fixes #ISSUE_NUMBER") (N/A - Typo fix)
- [x] Only one issue is addressed in this pull request
- [x] 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.

Co-authored-by: danko &lt;danko.pistek@gmail.com&gt;
Co-authored-by: sekyondaMeta &lt;127536312+sekyondaMeta@users.noreply.github.com&gt;
diff --git a/intermediate_source/pinmem_nonblock.py b/intermediate_source/pinmem_nonblock.py
@@ -127,7 +127,7 @@
 #   1. The device must have at least one free DMA (Direct Memory Access) engine. Modern GPU architectures such as Volterra,
 #      Tesla, or H100 devices have more than one DMA engine.
 #
-#   2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handles using
+#   2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handled using
 #      :class:`~torch.cuda.Stream`.
 #
 #   3. The source data must be in pinned memory.

Original file line number	Diff line number	Diff line change
`@@ -127,7 +127,7 @@`
`127`	`127`	`# 1. The device must have at least one free DMA (Direct Memory Access) engine. Modern GPU architectures such as Volterra,`
`128`	`128`	`# Tesla, or H100 devices have more than one DMA engine.`
`129`	`129`	`#`
`130`		`-# 2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handles using`
	`130`	`+# 2. The transfer must be done on a separate, non-default cuda stream. In PyTorch, cuda streams can be handled using`
`131`	`131`	# :class:`~torch.cuda.Stream`.
`132`	`132`	`#`
`133`	`133`	`# 3. The source data must be in pinned memory.`