}

  fun assertRequests(vararg expectedHosts: String?) {
    assertThat(requestedHosts).containsExactly(*expectedHosts)
    requestedHosts.clear()
  }

  /** Allocates and returns `count` fake IPv4 addresses like [255.0.0.100, 255.0.0.101].  */
  fun allocate(count: Int): List<InetAddress> {
    val from = nextAddress
    nextAddress += count
    return (from until nextAddress)
      .map {

ledTemporaryFile" class="external-link" target="_blank">`SpooledTemporaryFile`</a> (ein <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a> Objekt). Das ist das tatsächliche Python-Objekt, das Sie direkt anderen Funktionen oder Bibliotheken übergeben können, welche ein „file-like“-Objekt erwarten.

`UploadFile` hat die folgenden `async`hronen Methoden. Sie alle rufen die entsprechenden Methoden des darunterliegenden...

  /**
   * Returns a specific media subtype, such as "plain" or "png", "mpeg", "mp4" or "xml".
   */
  @get:JvmName("subtype") val subtype: String,
  /** Alternating parameter names with their values, like `["charset", "utf-8"]`. */
  internal val parameterNamesAndValues: Array<String>,
) {
  /**
   * Returns the charset of this media type, or [defaultValue] if either this media type doesn't

# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------
   

 *       doing something and finally closing the reader that was opened.
 * </ul>
 *
 * <p>Several methods in this class, such as {@link #readLines()}, break the contents of the source
 * into lines. Like {@link BufferedReader}, these methods break lines on any of {@code \n}, {@code
 * \r} or {@code \r\n}, do not include the line separator in each line and do not consider there to

      </phases>
    </lifecycle>
  </lifecycles>
</configuration>

We need to turn this list of phases into a set of plugin objects that have an XML representation like the following:

<plugins>
  <plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-resources-plugin</artifactId>
    <executions>
      <execution>
        <goals>

///

```Python hl_lines="9"
{!> ../../docs_src/header_params/tutorial003.py!}
```

////

If you communicate with that *path operation* sending two HTTP headers like:

```
X-Token: foo
X-Token: bar
```

The response would be like:

```JSON
{
    "X-Token values": [
        "bar",
        "foo"
    ]
}
```

## Recap

    val handshake = makeRequest(client)

    assertThat(handshake.cipherSuite).isIn(*expectedModernTls12CipherSuites.toTypedArray())

    // Probably something like
    // TLS_AES_128_GCM_SHA256
    // TLS_AES_256_GCM_SHA384
    // TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384
    // TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256
    // TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384

 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}
 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}