### Certificate Renewal

At some point in the future, each certificate would **expire** (about 3 months after acquiring it).

And then, there would be another program (in some cases it's another program, in some cases it could be the same TLS Termination Proxy) that would talk to Let's Encrypt, and renew the certificate(s).

        }

        if (getDependencySpec() == null) {
            throw new InvalidUserDataException("Dependency insight report cannot be generated because the dependency to show was not specified."
                + "\nIt can be specified from the command line, e.g: '" + getPath() + " --dependency someDep'");
        }
    }

    }

    void "dynamic method fails for unknown configuration"() {
        when:
        dependencyHandler.unknown("someDep")

        then:
        def e = thrown(MissingMethodException)
        e.message.startsWith('Could not find method unknown() for arguments [someDep] on ')
    }

    void "dynamic method fails for no args"() {
        when:
        dependencyHandler.someConf()

        assertTestsExecuted("ThingTest", "ok")
    }

    def "build on Java application project with no dependencies"() {
        given:
        settingsFile << """
            rootProject.name = 'someapp'
        """
        buildFile << """
            plugins { id 'application' }
            application.mainClass = 'Thing'
        """
        file("src/main/java/Thing.java") << """
            class Thing {

}

func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}
func.func @while_cond_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i1> {
  %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i1>

    func.return %0 : tensor<i32>
  }
  func.func @branch_0(%arg0: tensor<i32>) -> tensor<i32> {
    %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
    func.return %0 : tensor<i32>
  }
  func.func @branch_1(%arg0: tensor<i32>) -> tensor<i32> {
    %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>

        when:
        run('init', '--project-name', 'someApp', '--dsl', scriptDsl.id, '--incubating')

        then:
        def dslFixture = dslFixtureFor(scriptDsl)
        dslFixture.assertGradleFilesGenerated()
        dslFixture.settingsFile.assertContents(dslFixture.containsStringAssignment('rootProject.name', 'someApp'))

        when:
        run('help')

        then:

  }
  func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
    %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
    func.return %0 : tensor<i32>
  }
  func.func @while_cond_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i1> {
    %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i1>

    }
    func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
      %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
      func.return %0 : tensor<i32>
    }
    func.func @while_cond_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i1> {
      %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i1>

  }

}

// -----

// expected-error@+1 {{'tf_saved_model.semantics' must be on a module op}}
"some_dialect.some_op"() {tf_saved_model.semantics} : () -> ()

// -----

// expected-error@+1 {{unknown tf_saved_model dialect attribute 'tf_saved_model.not_a_real_op_attr'}}
"some_dialect.some_op"() {tf_saved_model.not_a_real_op_attr} : () -> ()

// -----

module attributes {tf_saved_model.semantics} {