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docs/ru/docs/async.md
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docs/pt/docs/alternatives.md
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docs/em/docs/async.md
👉 😐, 🔠 1️⃣ 🧹 (🔌 👆) 🔜 🕹, 🤸 👫 🍕 👨🏭. & 🏆 🛠️ 🕰 ✊ ☑ 👷 (↩️ ⌛), & 👷 💻 ⌛ <abbr title="Central Processing Unit">💽</abbr>, 👫 🤙 👫 ⚠ "💽 🎁". --- ⚠ 🖼 💽 🔗 🛠️ 👜 👈 🚚 🏗 🧪 🏭. 🖼: * **🎧** ⚖️ **🖼 🏭**.
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docs/en/docs/tutorial/extra-data-types.md
Prefer to use the `Annotated` version if possible. ```Python hl_lines="1 2 12-16" {!> ../../../docs_src/extra_data_types/tutorial001.py!} ``` Note that the parameters inside the function have their natural data type, and you can, for example, perform normal date manipulations, like: === "Python 3.10+" ```Python hl_lines="18-19" {!> ../../../docs_src/extra_data_types/tutorial001_an_py310.py!} ```
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docs/en/docs/async.md
And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a <abbr title="Central Processing Unit">CPU</abbr>, they call these problems "CPU bound". --- Common examples of CPU bound operations are things that require complex math processing. For example:
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docs/en/docs/advanced/security/oauth2-scopes.md
Because the `SecurityScopes` will have all the scopes declared by dependants, you can use it to verify that a token has the required scopes in a central dependency function, and then declare different scope requirements in different *path operations*. They will be checked independently for each *path operation*. ## Check it
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docs/ko/docs/async.md
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docs/es/docs/async.md
Y como la mayor parte del tiempo de ejecución lo coge el trabajo real (en lugar de esperar), y el trabajo en un sistema lo realiza una <abbr title = "Central Processing Unit. En español: Unidad Central de Procesamiento."> CPU </abbr>, a estos problemas se les llama "<abbr title="En español: atado a CPU.">CPU bound</abbr>". ---
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docs/de/docs/async.md
Und da die meiste Ausführungszeit durch tatsächliche Arbeit (anstatt durch Warten) in Anspruch genommen wird und die Arbeit in einem Computer von einer <abbr title="Central Processing Unit – Zentrale Recheneinheit">CPU</abbr> erledigt wird, werden diese Probleme als „CPU-lastig“ („CPU bound“) bezeichnet. ---
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docs/zh/docs/async.md
无论是否轮流执行(并发),都需要相同的时间来完成,而你也会完成相同的工作量。 但在这种情况下,如果你能带上 8 名前收银员/厨师,现在是清洁工一起清扫,他们中的每一个人(加上你)都能占据房子的一个区域来清扫,你就可以在额外的帮助下并行的更快地完成所有工作。 在这个场景中,每个清洁工(包括您)都将是一个处理器,完成这个工作的一部分。 由于大多数执行时间是由实际工作(而不是等待)占用的,并且计算机中的工作是由 <abbr title="Central Processing Unit">CPU</abbr> 完成的,所以他们称这些问题为"CPU 密集型"。 --- CPU 密集型操作的常见示例是需要复杂的数学处理。 例如: * **音频**或**图像**处理; * **计算机视觉**: 一幅图像由数百万像素组成,每个像素有3种颜色值,处理通常需要同时对这些像素进行计算;
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