},
		},
	}

	configs := []config.Config{}
	if c.destRule != nil {
		configs = append(configs, config.Config{
			Meta: config.Meta{
				GroupVersionKind: gvk.DestinationRule,
				Name:             "acme",
			},
			Spec: c.destRule,
		})
	}
	if c.sidecar != nil {
		configs = append(configs, config.Config{
			Meta: config.Meta{
				GroupVersionKind: gvk.Sidecar,
				Name:             "default",

					},
				},
			}

			var cfg *config.Config
			if tt.destRule != nil {
				cfg = &config.Config{
					Meta: config.Meta{
						GroupVersionKind: gvk.DestinationRule,
						Name:             "acme",
						Namespace:        "default",
					},
					Spec: tt.destRule,
				}
			}
			cg := NewConfigGenTest(t, TestOptions{
				Instances:      instances,
				ConfigPointers: []*config.Config{cfg},

	if err != nil {
		return nil, nil, fmt.Errorf("failed to generate serial number: %w", err)
	}

	template := x509.Certificate{
		SerialNumber: serialNumber,
		Subject: pkix.Name{
			Organization: []string{"Acme Co"},
		},
		NotBefore: notBefore,
		NotAfter:  notAfter,

		KeyUsage:              x509.KeyUsageKeyEncipherment | x509.KeyUsageDigitalSignature,

     */
    SOURCE_MEMBER_GENERATED,

    /**
     * A Kotlin declaration came from some .class file
     */
    LIBRARY,

    /**
     * A Kotlin declaration came from some Java source file
     */
    JAVA_SOURCE,

    /**
     * A Kotlin declaration came from some Java library
     */
    JAVA_LIBRARY,

    /**

					needPush = true
					break
				}
			}
		}
	}

	if pushType != FullPush && !needPush {
		log.Debugf("No push, either old endpoint health status did not change or new endpoint came with unhealthy status, %v", hostname)
		pushType = NoPush
	}

	ep.Shards[shard] = newIstioEndpoints

	// Check if ServiceAccounts have changed. We should do a full push if they have changed.

    arguments/keyword arguments comes from a keras input. Previously the
    functional api would only work if all of the elements in the first argument
    to the layer came from a keras input.
*   Clean up `BatchNormalization` layer's `trainable` property to act like
    standard python state when it's used inside `tf.functions` (frozen at

  }];

  let description = [{
This op uses the algorithm by Marsaglia et al. to acquire samples via
transformation-rejection from pairs of uniform and normal random variables.
See http://dl.acm.org/citation.cfm?id=358414
  }];

  let arguments = (ins
    Arg<TF_I32OrI64Tensor, [{1-D integer tensor. Shape of independent samples to draw from each