l'application efficacement et sans interruption ni problème.

Ceci contraste avec les étapes de **développement**, où vous êtes constamment en train de modifier le code, de le casser
et de le réparer, d'arrêter et de redémarrer le serveur de développement, _etc._

## Stratégies de déploiement

Le **Guide d'utilisation avancé**, qui s'appuie sur cette base, utilise les mêmes concepts et vous apprend quelques fonctionnalités supplémentaires.

Mais vous devez d'abord lire le **Tutoriel - Guide d'utilisation** (ce que vous êtes en train de lire en ce moment).

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

package http

import (
	"io"

	xioutil "github.com/minio/minio/internal/ioutil"
)

// DrainBody close non nil response with any response Body.
// convenient wrapper to drain any remaining data on response body.
//
// Subsequently this allows golang http RoundTripper
// to reuse the same connection for future requests.
func DrainBody(respBody io.ReadCloser) {

                    }
                }

                ids.add(0, artifact);
                node = node.parent;
            }
            trail = ids;
        }
        return trail;
    }

    public boolean isResolved() {
        return children != null;
    }

    /**
     * Test whether the node is direct or transitive dependency.
     *

		result := drain(input)
		if result != test.output {
			t.Errorf("%s: got %q expected %q", test.name, result, test.output)
		}
	}
}

// lines joins the arguments together as complete lines.
func lines(a ...string) string {
	return strings.Join(a, "\n") + "\n"
}

// drain returns a single string representing the processed input tokens.

            @Nullable Throwable error) {}

    /**
     * If this is an accumulating log, it will "drain" this instance. It returns the accumulated log entries, and
     * also "resets" this instance to empty (initial) state.
     */
    @Nonnull
    default List<Entry> drain() {
        return List.of();
    }

        requireNonNull(level, "level");
        requireNonNull(message, "message");
        entries.get().add(new Entry(level, message, error));
    }

    @Override
    public List<Entry> drain() {
        return entries.getAndSet(new CopyOnWriteArrayList<>());
    }

<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li>
<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>

func (s *Stream) Results(next func(b []byte) error) (err error) {
	done := false
	defer func() {
		if s.cancel != nil {
			s.cancel(err)
		}

		if !done {
			// Drain channel.
			for range s.responses {
			}
		}
	}()
	doneCh := s.ctx.Done()
	for {
		select {
		case <-doneCh:
			if err := context.Cause(s.ctx); !errors.Is(err, errStreamEOF) {
				return err
			}