properties:
              mtls:
                description: Mutual TLS settings for workload.
                properties:
                  mode:
                    description: |-
                      Defines the mTLS mode used for peer authentication.

                      Valid Options: DISABLE, PERMISSIVE, STRICT

            properties:
              mtls:
                description: Mutual TLS settings for workload.
                properties:
                  mode:
                    description: |-
                      Defines the mTLS mode used for peer authentication.

                      Valid Options: DISABLE, PERMISSIVE, STRICT

                - message: tls must not be specified for protocols ['HTTP', 'TCP',
                    'UDP']
                  rule: 'self.all(l, l.protocol in [''HTTP'', ''TCP'', ''UDP''] ?
                    !has(l.tls) : true)'
                - message: tls mode must be Terminate for protocol HTTPS
                  rule: 'self.all(l, (l.protocol == ''HTTPS'' && has(l.tls)) ? (l.tls.mode

			secrets: core.Secret{
				ObjectMeta: metav1.ObjectMeta{Name: "tls-cert"},
				Data: map[string][]byte{
					core.TLSCertKey: []byte("public key"),
				},
			},
			errorType: "FieldValueRequired",
		},
		"missing private key": {
			secrets: core.Secret{
				ObjectMeta: metav1.ObjectMeta{Name: "tls-cert"},
				Data: map[string][]byte{
					core.TLSCertKey: []byte("public key"),

            `tf.function`, and graph modes. This gets rid of the need for users
            to manually call `Model._set_inputs` when using Custom Training
            Loops(CTLs).
        *   Dynamic shapes are supported for generators by calling the Model on
            the first batch we "peek" from the generator. This used to happen