endpoints: []endpoint{
				{"10.0.1.1", testHostname},
				{"10.0.1.2", "host1"},
				{"10.0.1.3", "host2"},
			},
		},
		{
			name:                  "externalTrafficPolicy is set and there are local endpoints",
			externalTrafficPolicy: v1.ServiceExternalTrafficPolicyLocal,
			endpoints: []endpoint{
				{"10.0.1.1", testHostname},
				{"10.0.1.2", "host1"},

					},
					graphNodes:       []*node{makeNode(pod1ns1, withOwners(pod2ns2)), makeNode(pod2ns2)},
					absentOwnerCache: []objectReference{pod2ns1}, // cached absence of bad parent
				}),
				// 10,11: observe delete issued in step 8
				// final state: parent present in graph, no queued actions
				processEvent(makeDeleteEvent(pod1ns1)),
				assertState(state{

  %2 = "tf.Add"(%0, %1#1) : (tensor<f32>, tensor<?x8x10xf32>) -> tensor<?x8x10xf32>

        "strictly '[1.1, 1.4]'"             | '{strictly [1.1, 1.4]}'           | "versions of foo verified to run on platform Y" | '1.4'    | "didn't match versions 2.0, 1.5 because "
        "prefer '[1.0, 1.4]'; reject '1.4'" | '{prefer [1.0, 1.4]; reject 1.4}' | "1.4 has a critical bug"                        | '1.3'    | "rejected version 1.4 because "
    }

    def "doesn't report duplicate reasons"() {
        given:

			},
			errors: map[string]string{
				"timestamp('1000-00-00T00:00:00Z')":  "compilation failed: ERROR: <input>:1:11: invalid timestamp",
				"timestamp('1000-01-01T00:00:00ZZ')": "compilation failed: ERROR: <input>:1:11: invalid timestamp",
				"timestamp(-62135596801)":            "compilation failed: ERROR: <input>:1:11: invalid timestamp",
			},
		},
		{name: "enums",
			obj: map[string]interface{}{"enumStr": "Pending"},

    ```mlir

Note: each component can be installed in parallel with an existing Istio 1.0 or 1.1 installation in `istio-system`. The new components will not interfere with existing apps, but can interoperate, and it is possible to gradually move apps from Istio 1.0/1.1 to the new environments and across environments ( for example canary -> prod ) Note: there are still some cluster roles that may need to be fixed, most likely cluster permissions will need to move to the security component. ## Everything is Optional...

  %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4x!quant.uniform<i8:f32, 3.000000e+0:-100>>, tensor<3x3x4x2x!quant.uniform<i8:f32:3, {2.000000e+2, 3.000000e+3}>>) -> tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+0>>...

				},
			}
			gotReplicasChangeUp := getReplicasChangePerPeriod(60, hcUp.scaleUpEvents[tc.key])
			assert.Equal(t, tc.expectedReplicasChange, gotReplicasChangeUp)
			hcUp.storeScaleEvent(behaviorUp, tc.key, 10, 10+tc.replicaChange)
			if !assert.Len(t, hcUp.scaleUpEvents[tc.key], len(tc.newScaleEvents), "up: scale events differ in length") {
				return
			}
			for i, gotEvent := range hcUp.scaleUpEvents[tc.key] {

  auto shape = elements.getType().getShape();
  if (!shape.empty()) {
    // Checks that elements are essentially 1d.
    assert(elements.getNumElements() == shape.back());
  }
  std::vector<int64_t> shape_data = {1, 1, 1, 1};
  const int vector_length = elements.getNumElements();
  if (is_depthwise)
    shape_data[3] = vector_length;
  else
    shape_data[0] = vector_length;
  auto new_shape =