DiskID  string `msg:"id"`
	Metrics bool   `msg:"m"`
	NoOp    bool   `msg:"np"`
}

// DiskInfo is an extended type which returns current
// disk usage per path.
// The above means that any added/deleted fields are incompatible.
//
// The above means that any added/deleted fields are incompatible.
//
//msgp:tuple DiskInfo
type DiskInfo struct {
	Total      uint64
	Free       uint64
	Used       uint64
	UsedInodes uint64

    They are more relevant for older versions, before OpenAPI 3.1.0 was available.

    You can consider this a brief OpenAPI and JSON Schema **history lesson**. 🤓

!!! warning
    These are very technical details about the standards **JSON Schema** and **OpenAPI**.

without modification, are permitted provided that the following
conditions are met:

Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with
the distribution.

 *
 * <ul>
 *     <li>The automation is enabled for all PRs via the presence of 'always' in the appropriate map entry above.</li>
 * </ul>
 *
 * or
 *
 * <ul>
 *     <li>The author is a member of the Gradle organization,</li>
 *     <li>and has opted into the automation by adding their username to the appropriate map entry above.</li>
 * </ul>
 *
 * or
 *
 * <ul>

async with lifespan(app):
    await do_stuff()
```

When you create a context manager or an async context manager like above, what it does is that, before entering the `with` block, it will execute the code before the `yield`, and after exiting the `with` block, it will execute the code after the `yield`.

In our code example above, we don't use it directly, but we pass it to FastAPI for it to use it.

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization

modification, are permitted provided that the following conditions are
met:

   * Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
   * Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with the
distribution.

modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

In all the docs there are examples compatible with each version of Python (when there's a difference).

For example "**Python 3.6+**" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "**Python 3.9+**" means it's compatible with Python 3.9 or above (including 3.10, etc).

    implementation("org.gradle.kotlin.kotlin-dsl:org.gradle.kotlin.kotlin-dsl.gradle.plugin:4.4.0")
    // This Kotlin version should only be updated when updating the above kotlin-dsl version
    implementation("org.jetbrains.kotlin:kotlin-gradle-plugin:1.9.23")
    implementation("org.gradle.kotlin:gradle-kotlin-dsl-conventions")
    implementation("org.gradle:test-retry-gradle-plugin:1.5.2")