/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model { #header-parameters-with-a-pydantic-model }

Declare the **header parameters** that you need in a **Pydantic model**, and then declare the parameter as `Header`:

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

     * <p>
     * The first version in a minor series will never have any preceding versions, so there's no
     * need to fetch tags and examine the repository state in the past. This applies when the
     * version is a release version, a snapshot, or the first alpha version. Subsequent alphas,
     * betas and release candidates need to check the previous prelease tags.
     *
     * @param versionString the version string to check

 * classes and the classes they depend on. The classes are not relocated, they all
 * remain in their original namespace. This reduces the final Gradle distribution
 * size and makes us more conscious of which parts of a library we really need.
 *
 * WARNING: if you decide to do the minification by hand, make sure that you cover all paths of loading classes:
 * reflection, dynamic loading, etc. and understand how the library works internally.

        final List<String> output = new ArrayList<>(lines.size());

        // This flag provides a way to handle properties whose values are split
        // over multiple lines and we need to omit those properties.
        boolean skipNext = false;

        for (String line : lines) {
            if (skipNext) {
                if (line.endsWith("\\") == false) {
                    skipNext = false;

          // it is kind of lame that we need to construct a decoder to access this value.
          // if this is a concern we could add special cases for some known charsets (like utf8)
          // or we could avoid inputstreamreader and use the decoder api directly
          // TODO(lukes): in a real implementation we would need to handle overflow conditions

    // exponent == floor(log2(abs(x)))
    if (exponent < Long.SIZE - 1) {
      return x.longValue();
    } else if (exponent > MAX_EXPONENT) {
      return x.signum() * POSITIVE_INFINITY;
    }

    /*
     * We need the top SIGNIFICAND_BITS + 1 bits, including the "implicit" one bit. To make rounding
     * easier, we pick out the top SIGNIFICAND_BITS + 2 bits, so we have one to help us round up or

          // it is kind of lame that we need to construct a decoder to access this value.
          // if this is a concern we could add special cases for some known charsets (like utf8)
          // or we could avoid inputstreamreader and use the decoder api directly
          // TODO(lukes): in a real implementation we would need to handle overflow conditions

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

 * href="https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)">graph</a>-structured data.
 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * topological sort) that only need a way of accessing the predecessors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :

You can also use regular `def` functions (without `async`), and use `yield` the same way.

{* ../../docs_src/stream_data/tutorial001_py310.py ln[26:29] hl[27] *}

### No Annotation { #no-annotation }

You don't really need to declare the return type annotation for streaming binary data.