diff --git a/content/javascript/concepts/dom-manipulation/terms/querySelectorAll/querySelectorAll.md b/content/javascript/concepts/dom-manipulation/terms/querySelectorAll/querySelectorAll.md
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----
-Title: '.querySelectorAll()'
-Description: 'Returns a static (non-live) NodeList of all elements in the document that match the given CSS selectors.'
-Subjects:
-  - 'Code Foundations'
-  - 'Web Development'
-Tags:
-  - 'Methods'
-  - 'Node'
-  - 'Selectors'
-CatalogContent:
-  - 'introduction-to-javascript'
-  - 'paths/front-end-engineer-career-path'
----
-
-In JavaScript, the **`.querySelectorAll()`** method under the `document` object returns a static (not live) `NodeList` of all elements that match the given group of [selectors](https://www.codecademy.com/resources/docs/css/selectors).
-
-## Syntax
-
-```pseudo
-document.querySelectorAll(selectors);
-```
-
-- `selectors`: Represents a string containing one or more CSS selectors used to match elements in the document. It follows the same rules as CSS selectors and can include:
-  - Type selectors (`div`, `p`, `span`)
-  - Class selectors (`.class-name`)
-  - ID selectors (`#id-name`)
-  - Attribute selectors (`[type="text"]`, `[disabled]`)
-  - Combinations (`div p`, `.container > p`, `ul > li:first-child`)
-
-## Examples
-
-### Example 1
-
-In this example, a `NodeList` of all `<p>` elements in the document is obtained:
-
-```js
-const matches = document.querySelectorAll('p');
-```
-
-### Example 2
-
-The following example returns a list of all `<div>` elements in the document with a class of either `note` or `alert`:
-
-```js
-const matches = document.querySelectorAll('div.note, div.alert');
-```
-
-### Example 3
-
-In this example, a list of `<p>` elements is obtained, whose immediate parent is a `<div>` with the class `highlighted`, and which are inside a container with the ID `test`:
-
-```js
-const container = document.querySelector('#test');
-const matches = container.querySelectorAll('div.highlighted > p');
-```
diff --git a/content/pytorch/concepts/tensor-operations/terms/trunc/trunc.md b/content/pytorch/concepts/tensor-operations/terms/trunc/trunc.md
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+---
+Title: '.trunc()'
+Description: 'Removes the fractional part of each element by truncating toward zero.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Arrays'
+  - 'Data Structures'
+  - 'Deep Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, **`.trunc()`** removes the fractional component of each element in a tensor by truncating values toward zero. If the input tensor has an integer dtype, the values remain unchanged.
+
+## Syntax
+
+```pseudo
+torch.trunc(input, out=None)
+```
+
+Alternative tensor method:
+
+```pseudo
+tensor.trunc()
+```
+
+**Parameters:**
+
+- `input`: The input tensor.
+- `out` (optional): Output tensor to write results into.
+
+**Return value:**
+
+Returns a tensor of the same dtype as `input`, with fractional parts removed (toward zero). Floating-point tensors are truncated, while integer tensors are unaffected.
+
+## Example 1: Basic truncation on 1D tensor
+
+In this example, `torch.trunc()` removes the fractional parts of values in a 1D tensor, truncating each number toward zero:
+
+```py
+import torch
+
+x = torch.tensor([1.9, -2.1, 0.0, 3.5])
+y = torch.trunc(x)
+
+print(y)
+```
+
+The above code will result in the following output:
+
+```shell
+tensor([ 1., -2.,  0.,  3.])
+```
+
+## Example 2: Using the `out` parameter
+
+In this example, `torch.trunc()` uses the `out` parameter to store the truncated results in a preallocated tensor:
+
+```py
+import torch
+
+a = torch.tensor([3.4742, 0.5466, -0.8008, -0.9079])
+out = torch.empty_like(a)
+torch.trunc(a, out=out)
+
+print(out)
+```
+
+A sample output might be:
+
+```shell
+tensor([ 3.,  0., -0., -0.])
+```
+
+## Example 3: Method form on a 2D tensor
+
+In this example, the `.trunc()` tensor method truncates all elements of a 2D tensor toward zero:
+
+```py
+import torch
+
+m = torch.tensor([[ 2.8, -1.2,  0.4],
+                  [-3.9,  7.1, -0.5]])
+n = m.trunc()
+print(n)
+```
+
+The above code will result in the following output:
+
+```shell
+tensor([[ 2., -1.,  0.],
+        [-3.,  7., -0.]])
+```