assertFalse(InetAddresses.isInetAddress("016.016.016.016"));
  }

  public void testForStringIPv4Input() throws UnknownHostException {
    String ipStr = "192.168.0.1";
    // Shouldn't hit DNS, because it's an IP string literal.
    InetAddress ipv4Addr = InetAddress.getByName(ipStr);
    assertEquals(ipv4Addr, InetAddresses.forString(ipStr));
    assertTrue(InetAddresses.isInetAddress(ipStr));
  }

   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each
   * other. But Atomic objects residing in arrays will tend to be
   * placed adjacent to each other, and so will most often share
   * cache lines (with a huge negative performance impact) without
   * this precaution.
   *
   * In part because Cells are relatively large, we avoid creating

            unifiedNullChecks = true,
            metadataVersion = metadataVersion,
            // Technically we should use JvmDefaultMode.ALL_INCOMPATIBLE because Kapt4 always uses `-Xjvm-default=all`, but it would make
            // the majority of tests fail because metadata of each interface will have a flag set, compared to Kapt3.
            jvmDefaultMode = JvmDefaultMode.DISABLE,
            stringTable,
            null,

    private val owner: FirJavaClass,
) : FirCallableFilteringScope(delegate) {
    init {
        // The `isDeclared` check is based on class IDs. Local classes don't have proper class IDs, but because this scope is used to
        // represent Java classes viewed from Kotlin code, we shouldn't be able to encounter any local Java classes.
        require(!owner.isLocal) {

 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in
 * a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still runs
 * quickly, since the row key is provided. However, {@code column(columnKey).size()} takes longer,
 * since an iteration across all row keys occurs.
 *
 * <p>Because a {@code TreeBasedTable} has unique sorted values for a given row, both {@code

      closeLater(ErrorCode.CANCEL)
      connection.sendDegradedPingLater()
    }

    override fun newTimeoutException(cause: IOException?): IOException {
      return SocketTimeoutException("timeout").apply {
        if (cause != null) {
          initCause(cause)
        }
      }
    }

    @Throws(IOException::class)
    fun exitAndThrowIfTimedOut() {

		{ // supplying valid pod name retrieves Envoy config (fails because we don't check in Envoy config unit tests)
			execClientConfig: loggingConfig,
			args:             strings.Split("clusters httpbin-794b576b6c-qx6pf", " "),
			expectedString:   `config dump has no configuration type`,
			wantException:    true,
		},
		{ // supplying valid pod name retrieves Envoy config (fails because we don't check in Envoy config unit tests)

ItemsService.createItemItemsPost({name: "Plumbus", price: 5})
```

...that's because the client generator uses the OpenAPI internal **operation ID** for each *path operation*.

OpenAPI requires that each operation ID is unique across all the *path operations*, so FastAPI uses the **function name**, the **path**, and the **HTTP method/operation** to generate that operation ID, because that way it can make sure that the operation IDs are unique.

   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each
   * other. But Atomic objects residing in arrays will tend to be
   * placed adjacent to each other, and so will most often share
   * cache lines (with a huge negative performance impact) without
   * this precaution.
   *
   * In part because Cells are relatively large, we avoid creating

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.