should be used with caution when working with untrusted models.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

    * **Rapide** : Très hautes performances, comparables à **NodeJS** ou **Go** (grâce à Starlette et Pydantic).
    * **Intuitif** : Excellent support des éditeurs. <abbr title="aussi appelée auto-complétion, autocomplétion, IntelliSense...">Complétion</abbr> partout. Moins de temps passé à déboguer.
    * **Facile** : Fait pour être facile à utiliser et apprendre. Moins de temps passé à lire de la documentation.

                    writer.write(newContent);
                }
            } catch (IOException e) {
                logError("Failed to write to file", e);
                throw e; // Re-throw to ensure the compilation fails
            }
        } else {
            logMessage(Diagnostic.Kind.NOTE, "Content unchanged. Skipping file update.");
        }
    }

    /**

    assertThat(min(2, 1, reverse)).isEqualTo(2);
    assertThat(max(1, 2, reverse)).isEqualTo(1);
    assertThat(max(2, 1, reverse)).isEqualTo(1);
  }

  /**
   * Fails compilation if the signature of min and max is changed to take {@code Comparator<T>}
   * instead of {@code Comparator<? super T>}.
   */
  public void testMinMaxWithSupertypeComparator() {

    private static final double TIMING_TOLERANCE = 2.0; // 200% tolerance - JVM timing is inherently variable for timing variations

    @BeforeEach
    public void setUp() {
        // Warm up JVM to reduce JIT compilation effects
        for (int i = 0; i < 100; i++) {
            NtlmPasswordAuthenticator auth1 = new NtlmPasswordAuthenticator("testdomain", "testuser", "password123");

    "groups" : [ {
      "testProject" : "largeJavaMultiProject",
      "comment" : "We only test the multi-project here since for the monolithic project we would have no cache hits. This would mean we actually would test incremental compilation.",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.

    work need not make them do so.

  A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not

      True, ('Would you like to override eigen strong inline for some C++ '
             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):
    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521

    nums.add(3);
    Predicate<Integer> isThree = Predicates.in(nums);
    assertFalse(isThree.apply(3));
  }

  /*
   * Tests that compilation will work when applying explicit types.
   */
  @SuppressWarnings("unused") // compilation test
  public void testIn_compilesWithExplicitSupertype() {
    Collection<Number> nums = ImmutableSet.of();
    Predicate<Number> p1 = Predicates.in(nums);