# Note that numpy 2.1.0 does not support python 3.9
numpy >= 2.0.0, < 2.2.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

     *
     * @param <K> the key type of {@link ConcurrentHashMap}
     * @param <V> the value type of {@link ConcurrentHashMap}
     * @param initialCapacity the initial capacity
     * @param loadFactor the load factor threshold for resizing
     * @param concurrencyLevel the estimated number of threads for concurrent updates
     * @return a new instance of {@link ConcurrentHashMap}

        ArgumentCaptor<SmbComBlankResponse> respCap = ArgumentCaptor.forClass(SmbComBlankResponse.class);
        verify(tree, times(1)).send(reqCap.capture(), respCap.capture(), eq(RequestParam.NO_RETRY));
        assertTrue(reqCap.getValue() instanceof SmbComClose, "Expected SmbComClose request");
        assertNotNull(respCap.getValue(), "Expected SmbComBlankResponse");

      options:
        - "Yes"
        - "No"
    validations:
      required: true

  - type: markdown
    attributes:
      value: |

					r: r,
				}
				n, err := xioutil.Copy(xioutil.Discard, &fbr)
				r.Close()
				if err == nil {
					response := time.Since(t)
					ttfb := time.Since(*fbr.t)
					// Only capture success criteria - do not
					// have to capture failed reads, truncated
					// reads etc.
					atomic.AddUint64(&totalBytesRead, uint64(n))
					mu.Lock()
					downloadTimes = append(downloadTimes, response)

    }

    void sleepMillis(int millis) {
      sleepMicros("U", MILLISECONDS.toMicros(millis));
    }

    void sleepMicros(String caption, long micros) {
      instant += MICROSECONDS.toNanos(micros);
      events.add(caption + String.format(Locale.ROOT, "%3.2f", micros / 1000000.0));
    }

    @Override
    protected void sleepMicrosUninterruptibly(long micros) {

    }

    void sleepMillis(int millis) {
      sleepMicros("U", MILLISECONDS.toMicros(millis));
    }

    void sleepMicros(String caption, long micros) {
      instant += MICROSECONDS.toNanos(micros);
      events.add(caption + String.format(Locale.ROOT, "%3.2f", micros / 1000000.0));
    }

    @Override
    protected void sleepMicrosUninterruptibly(long micros) {

- Already compressed objects are not fit for compression since they do not have compressible patterns.
Such objects do not produce efficient [`LZ compression`](https://en.wikipedia.org/wiki/LZ77_and_LZ78)
which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.

ar
are
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aşadar
asemenea
asta
ăsta
astăzi
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ăstea
ăştia
asupra
aţi
au
avea
avem
aveţi
azi
bine
bucur
bună
ca
că
căci
când
care
cărei
căror
cărui
cât
câte
câţi
către
câtva
ce
cel
ceva
chiar
cînd
cine
cineva
cît
cîte
cîţi
cîtva
contra
cu
cum
cumva
curând
curînd
da
dă
dacă
dar

```Python
images: List[Image]
```

como em:

{* ../../docs_src/body_nested_models/tutorial008.py hl[15] *}

## Suporte de editor em todo canto

E você obtém suporte do editor em todos os lugares.

Mesmo para itens dentro de listas:

<img src="/img/tutorial/body-nested-models/image01.png">