python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"
bazel run --HERMETIC_PYTHON_VERSION=3.13 //tensorflow/tools/pip_package:setup_py_binary -- bdist_wheel --dist-dir "$TFCI_OUTPUT_DIR"

# Get the name of the pure python wheel that was built. This should
# resolve to either
# 1. tensorflow-a.b.c-py3-none-any.whl  or, for nightly builds,

/// info

Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

///

## Bodies of pure lists { #bodies-of-pure-lists }

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python

        // ensure that bouncycastle is on classpath for the all of test types, must happen in evaluateAfter since the rest tests explicitly
        // set the class path to help maintain pure black box testing, and here we are adding to that classpath
        tasks.withType(Test).configureEach { Test test ->
          test.setClasspath(test.getClasspath().plus(extraFipsJars))
        }
      }

But if you want to **stream pure binary data** or strings, here's how you can do it.

/// info

Added in FastAPI 0.134.0.

///

## Use Cases { #use-cases }

You could use this if you want to stream pure strings, for example directly from the output of an **AI LLM** service.

import jcifs.smb1.util.Hexdump;

/**
 * Unit test for {@link Trans2QueryFSInformation}. The class contains mostly
 * trivial wire-format helpers and a custom {@code toString()} implementation.
 * All tests are pure unit tests – no network or file system access is
 * required.
 */
class Trans2QueryFSInformationTest {

    /** Small helper to create a byte buffer larger than the maximum expected

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);

你可以定義任意深度的巢狀模型：

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info

請注意，`Offer` 具有一個 `Item` 的列表，而每個 `Item` 又有一個可選的 `Image` 列表。

///

## 純列表的本文 { #bodies-of-pure-lists }

如果你期望的 JSON 本文頂層值是一個 JSON `array`（Python 的 `list`），可以像在 Pydantic 模型中那樣，直接在函式參數上宣告其型別：

```Python
images: list[Image]
```

如下所示：

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info | 정보

`Offer`가 `Item`의 리스트를 가지고, 그 `Item`이 다시 선택 사항인 `Image` 리스트를 갖는지 주목하세요

///

## 순수 리스트의 본문 { #bodies-of-pure-lists }

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)라면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

```Python
images: list[Image]
```

이를 아래처럼:

你可以定义任意深度的嵌套模型：

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info | 信息

请注意 `Offer` 拥有一组 `Item` 而反过来 `Item` 又是一个可选的 `Image` 列表是如何发生的。

///

## 纯列表请求体 { #bodies-of-pure-lists }

如果你期望的 JSON 请求体的最外层是一个 JSON `array`（即 Python `list`），则可以在路径操作函数的参数中声明此类型，就像声明 Pydantic 模型一样：

```Python
images: list[Image]
```

例如：