`num_tokens`.
        *   `Discretization` added `num_bins` argument for learning bins
            boundaries through calling `adapt` on a dataset. Renamed `bins`
            argument to `bin_boundaries` for specifying bins without `adapt`.
    *   Improvements to model saving/loading:
        *   `model.load_weights` now accepts paths to saved models.

    /**
     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:1.0
     * a:2.0
     * </pre>
     */
    @Test
    void testEqual() {
        ResolutionNode a1n = createResolutionNode(a1);
        ResolutionNode a2n = createResolutionNode(a2);

        assertResolveConflict(a2n, a1n, a2n);
    }

    /**
     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:2.0

    /**
     * Tests that <code>a:1.0</code> wins in the scenario:
     * <pre>
     * a:1.0
     * a:2.0
     * </pre>
     */
    @Test
    void testEqual() {
        ResolutionNode a1n = createResolutionNode(a1);
        ResolutionNode a2n = createResolutionNode(a2);

        assertResolveConflict(a1n, a1n, a2n);
    }

    /**
     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:2.0

    /**
     * Tests that <code>a:1.0</code> wins in the scenario:
     * <pre>
     * a:1.0
     * a:2.0
     * </pre>
     */
    @Test
    void testEqual() {
        ResolutionNode a1n = createResolutionNode(a1);
        ResolutionNode a2n = createResolutionNode(a2);

        assertResolveConflict(a1n, a1n, a2n);
    }

    /**
     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:2.0

    /**
     * Tests that <code>a:1.0</code> wins in the scenario:
     * <pre>
     * a:1.0
     * a:2.0
     * </pre>
     */
    @Test
    void testEqual() {
        ResolutionNode a1n = createResolutionNode(a1);
        ResolutionNode a2n = createResolutionNode(a2);

        assertResolveConflict(a1n, a1n, a2n);
    }

    /**
     * Tests that <code>a:1.0</code> wins in the scenario:
     * <pre>
     * a:2.0

            res.setEntry(resEntry);

            res.setScope(requestedScope);

            for (MetadataGraphVertex v : vertices) {
                final List<MetadataGraphEdge> ins = graph.getIncidentEdges(v);
                final MetadataGraphEdge edge = cleanEdges(v, ins, requestedScope);

                if (edge == null) { // no edges - don't need this vertex anymore
                    if (entry.equals(v)) { // unless it's an entry point.

        assertEquals(createSet(new Object[] {a.artifact, modifiedC}), res.getArtifacts(), "Check artifact list");
        Artifact artifact = getArtifact("c", res.getArtifacts());
        // local wins now, and irrelevant if not local as test/provided aren't transitive
        // assertEquals( Artifact.SCOPE_COMPILE, artifact.getArtifactScope(), "Check artifactScope" );

	if opts == nil {
		opts = &NewFakeContextOption{}
	}
	ns := opts.Namespace
	ins := opts.IstioNamespace
	return &fakeInstance{
		clients: map[string]kube.CLIClient{},
		rootFlags: &RootFlags{
			kubeconfig:       ptr.Of[string](""),
			configContext:    ptr.Of[string](""),
			namespace:        &ns,
			istioNamespace:   &ins,
			impersonate:      ptr.Of[string](""),
			impersonateUID:   ptr.Of[string](""),

### <a href="https://apispec.readthedocs.io/en/stable/" class="external-link" target="_blank">APISpec</a>

Marshmallow e Webargs fornecem validação, análise e serialização como plug-ins.

Mas a documentação ainda está faltando. Então APISpec foi criado.

APISpec tem plug-ins para muitos frameworks (e tem um plug-in para Starlette também).

    }

    /**
     * This test has
     *   - a BOM import which manages dep to 0.3
     *   - another BOM import which manages the dep to 0.1
     * This <i>currently</i> results in 0.3 (first wins) and a warning
     */
    @Test
    void testManagedDependencyTwoImports() throws Exception {
        ModelBuilder builder = new DefaultModelBuilderFactory().newInstance();
        assertNotNull(builder);