--enable-shared \
      --enable-threads=posix \
      --with-default-libstdcxx-abi=${LIBSTDCXX_ABI} \
      --with-gcc-major-version-only \
      --with-linker-hash-style="gnu" \
      --with-tune="generic" \
      && \
    make -j 42 && \
    make install


# Create the devtoolset libstdc++ linkerscript that links dynamically against
# the system libstdc++ 4.4 and provides all other symbols statically.

      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {
    // use a dense table if more than half of the cells have values
    // TODO(gak): tune this condition based on empirical evidence
    return (cellList.size() > (((long) rowSpace.size() * columnSpace.size()) / 2))
        ? new DenseImmutableTable<R, C, V>(cellList, rowSpace, columnSpace)

Verilerin "biçimini" niteliklere sahip sınıflar olarak düzenlersiniz.

Ve her niteliğin bir türü vardır.

Sınıfın bazı değerlerle bir örneğini oluşturursunuz ve değerleri doğrular, bunları uygun türe dönüştürür ve size tüm verileri içeren bir nesne verir.

Ve ortaya çıkan nesne üzerindeki bütün editör desteğini alırsınız.

Resmi Pydantic dokümanlarından alınmıştır:

```Python

policy/control/telemetry - so testing/staging of new settings and versions can be performed
by a different role than the prod version.

The intended users of this repo are users running Istio in production who want to select, tune
and understand each binary that gets deployed, and select which combination to use.

Note: each component can be installed in parallel with an existing Istio 1.0 or 1.1 installation in

Eu nunca fui capaz de usar ele num projeto inteiro, por não ter integração de segurança, então, eu não pude substituir todos os recursos que eu tinha com os geradores _full-stack_ baseados no Flask-apispec. Eu tive em minha gaveta de projetos a idéia de criar um _pull request_ adicionando essa funcionalidade.

Mas então, o foco do projeto mudou.

Ele não era mais um framework web API, como o criador precisava focar no Starlette.

	// type of i is int
	// type of s is string
	// s == a[i]
	g(i, s)
}

var key string
var val interface{}  // element type of m is assignable to val
m := map[string]int{"mon":0, "tue":1, "wed":2, "thu":3, "fri":4, "sat":5, "sun":6}
for key, val = range m {
	h(key, val)
}
// key == last map key encountered in iteration
// val == map[key]

var ch chan Work = producer()

SSS",/\d\d:\d\d:\d\d,\d+/],["HH:mm:ss",/\d\d:\d\d:\d\d/],["HH:mm",/\d\d:\d\d/],["HHmmss.SSSS",/\d\d\d\d\d\d\.\d+/],["HHmmss,SSSS",/\d\d\d\d\d\d,\d+/],["HHmmss",/\d\d\d\d\d\d/],["HHmm",/\d\d\d\d/],["HH",/\d\d/]],ya=/^\/?Date\((-?\d+)/i,fa=/^(?:(Mon|Tue|Wed|Thu|Fri|Sat|Sun),?\s)?(\d{1,2})\s(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s(\d{2,4})\s(\d\d):(\d\d)(?::(\d\d))?\s(?:(UT|GMT|[ECMP][SD]T)|([Zz])|([+-]\d{4}))$/,pa={UT:0,GMT:0,EDT:-240,EST:-300,CDT:-300,CST:-360,MDT:-360,MST:-420,PDT:-42...

	// type of i is int
	// type of s is string
	// s == a[i]
	g(i, s)
}

var key string
var val interface{}  // element type of m is assignable to val
m := map[string]int{"mon":0, "tue":1, "wed":2, "thu":3, "fri":4, "sat":5, "sun":6}
for key, val = range m {
	h(key, val)
}
// key == last map key encountered in iteration
// val == map[key]

var ch chan Work = producer()

    *   Add `tf.contrib.data.prefetch_to_device`, which enables prefetching
        dataset elements to GPU memory.
    *   Add `tf.contrib.data.AUTOTUNE`, which allows the tf.data runtime to
        automatically tune the prefetch buffer sizes based on your system and
        environment.
    *   Add `tf.contrib.data.make_csv_dataset` for building datasets of CSV
        files.
*   Eager Execution: