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  1. docs/em/docs/tutorial/extra-models.md

        ```
    
    ## `Union` ⚖️ `anyOf`
    
    👆 💪 📣 📨 `Union` 2️⃣ 🆎, 👈 ⛓, 👈 📨 🔜 🙆 2️⃣.
    
    ⚫️ 🔜 🔬 🗄 ⏮️ `anyOf`.
    
    👈, ⚙️ 🐩 🐍 🆎 🔑 <a href="https://docs.python.org/3/library/typing.html#typing.Union" class="external-link" target="_blank">`typing.Union`</a>:
    
    !!! note
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  2. docs/en/docs/tutorial/extra-models.md

        ```
    
    ## `Union` or `anyOf`
    
    You can declare a response to be the `Union` of two types, that means, that the response would be any of the two.
    
    It will be defined in OpenAPI with `anyOf`.
    
    To do that, use the standard Python type hint <a href="https://docs.python.org/3/library/typing.html#typing.Union" class="external-link" target="_blank">`typing.Union`</a>:
    
    !!! note
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  3. docs/ru/docs/tutorial/extra-models.md

        ```
    
    ## `Union` или `anyOf`
    
    Вы можете определить ответ как `Union` из двух типов. Это означает, что ответ должен соответствовать одному из них.
    
    Он будет определён в OpenAPI как `anyOf`.
    
    Для этого используйте стандартные аннотации типов в Python <a href="https://docs.python.org/3/library/typing.html#typing.Union" class="external-link" target="_blank">`typing.Union`</a>:
    
    !!! note "Примечание"
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  4. docs/pt/docs/tutorial/extra-models.md

        ```
    
    ## `Union` ou `anyOf`
    
    Você pode declarar uma resposta como o `Union` de dois tipos, o que significa que a resposta seria qualquer um dos dois.
    
    Isso será definido no OpenAPI com `anyOf`.
    
    Para fazer isso, use a dica de tipo padrão do Python <a href="https://docs.python.org/3/library/typing.html#typing.Union" class="external-link" target="_blank">`typing.Union`</a>:
    
    !!! note
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  5. docs/de/docs/tutorial/extra-models.md

    !!! note "Hinweis"
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  6. src/cmd/vendor/golang.org/x/tools/internal/typeparams/coretype.go

    // type restrictions of a type, if any.
    //
    // For all types other than *types.TypeParam, *types.Interface, and
    // *types.Union, this is just a single term with Tilde() == false and
    // Type() == typ. For *types.TypeParam, *types.Interface, and *types.Union, see
    // below.
    //
    // Structural type restrictions of a type parameter are created via
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  7. docs/en/docs/python-types.md

        And the same as with Python 3.8, from the `typing` module:
    
        * `Union`
        * `Optional` (the same as with Python 3.8)
        * ...and others.
    
        In Python 3.10, as an alternative to using the generics `Union` and `Optional`, you can use the <abbr title='also called "bitwise or operator", but that meaning is not relevant here'>vertical bar (`|`)</abbr> to declare unions of types, that's a lot better and simpler.
    
    === "Python 3.9+"
    
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  8. src/go/types/termlist.go

    				// in normal form.)
    				if u1.typ == nil {
    					return allTermlist
    				}
    				xi = u1
    				used[j] = true // xj is now unioned into xi - ignore it in future iterations
    			}
    		}
    		rl = append(rl, xi)
    	}
    	return rl
    }
    
    // union returns the union xl ∪ yl.
    func (xl termlist) union(yl termlist) termlist {
    	return append(xl, yl...).norm()
    }
    
    // intersect returns the intersection xl ∩ yl.
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  9. docs/zh/docs/tutorial/extra-models.md

        ```
    
    ## `Union` 或者 `anyOf`
    
    响应可以声明为两种类型的 `Union` 类型,即该响应可以是两种类型中的任意类型。
    
    在 OpenAPI 中可以使用 `anyOf` 定义。
    
    为此,请使用 Python 标准类型提示 <a href="https://docs.python.org/3/library/typing.html#typing.Union" class="external-link" target="_blank">`typing.Union`</a>:
    
    !!! note "笔记"
    
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  10. tensorflow/cc/experimental/libtf/value.h

    /// @brief The TaggedValue struct that supports Python-like behavior in C++.
    ///
    /// The TaggedValue struct implements a tagged union data structure
    /// (https://en.wikipedia.org/wiki/Tagged_union) in the TensorFlow C++ API. It
    /// contains a `Type` enum (sometimes referred to as a "tag")
    /// and a `Data` union for holding values.
    
    #ifndef TENSORFLOW_CC_EXPERIMENTAL_LIBTF_VALUE_H_
    #define TENSORFLOW_CC_EXPERIMENTAL_LIBTF_VALUE_H_
    
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