Protocol: ptr.To(v1.ProtocolUDP),
		}}
	}
	complexAfter1_s1 := func(eps *discovery.EndpointSlice) {
		eps.Endpoints = []discovery.Endpoint{{
			Addresses: []string{"1.1.1.1"},
		}, {
			Addresses: []string{"1.1.1.11"},
		}}
		eps.Ports = []discovery.EndpointPort{{
			Name:     ptr.To("p11"),
			Port:     ptr.To[int32](11),
			Protocol: ptr.To(v1.ProtocolUDP),
		}}
	}

  %5 = 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<?x28x28x1xf32>, tensor<3x3x1x16xf32>)...

   // Unsupported strides

						Protocol: "HTTP",
						Name:     "uds",
					},
					Bind:            "1.1.1.1",
					DefaultEndpoint: "127.0.0.1:8083",
				},
				{
					Port: &networking.SidecarPort{
						Number:   8084,
						Protocol: "HTTP",
						Name:     "uds",
					},
					Bind:            "1.1.1.1",
					DefaultEndpoint: "127.0.0.1:8084",
				},
				{
					// not conflict with service port

    %10 = "tf.Cast"(%8) {Truncate = false, device = ""} : (tensor<2x3x3x3x2xi32>) -> tensor<2x3x3x3x2xf32>
    %11 = "tf.Conv3D"(%9, %10) {device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]} : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>
    %12 = "tf.Cast"(%11) {device = ""} : (tensor<1x3x2x3x2xf32>) -> tensor<1x3x2x3x2xi32>

    if use_variable_for_filter:
      filters = variables.Variable(filters)

    output_tensor = nn_ops.conv2d(
        in_placeholder,
        filters,
        strides=[1, 1, 2, 1],
        dilations=[1, 1, 1, 1],
        padding='SAME',
        data_format='NHWC',
    )

    return in_placeholder, output_tensor

  def _create_simple_tf1_gather_model(

      dilations: Sequence[int] = None,
      has_func_alias: bool = False,
  ):
    input_shape = (None, 3, 4, 3) if input_shape_dynamic else (1, 3, 4, 3)
    filter_shape = (2, 3, 3, 2)
    strides = (1, 1, 1, 1)
    model = self._create_conv2d_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,
        bias_fn,
        activation_fn,
        has_batch_norm,
        strides,

				},
			},
			ContainerStatuses: []corev1.ContainerStatus{
				{
					Name: "container-1",
				},
				{
					Name: "container-2",
				},
			},
			PodIP:  "1.1.1.1",
			HostIP: "1.1.1.1",
			Phase:  corev1.PodRunning,
		},
	}

	expectPod := &corev1.Pod{
		TypeMeta: metav1.TypeMeta{
			Kind:       "Pod",
			APIVersion: "v1",
		},

				{
					Port: &networking.SidecarPort{
						// A port that is not in any of the services
						Number:   9000,
						Protocol: "HTTP",
						Name:     "something",
					},
					Bind:  "1.1.1.1",
					Hosts: []string{"*/bookinfo.com"},
				},
				{
					Port: &networking.SidecarPort{
						// Unix domain socket listener
						Number:   0,
						Protocol: "HTTP",
						Name:     "something",

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>