func.return %0 : tensor<1x1x1x16xf32>
}

// -----

// CHECK-LABEL: func @testValidConv2D
func.func @testValidConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32> {

  %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>>...

  %0 = "tf.Conv3D"(%arg0, %arg1) {padding = "SAME", strides = [2, 1, 1, 1, 1]} : (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
  func.return %0: tensor<?x?x?x?x?xf32>
  // CHECK-LABEL: conv3d_invalid_strides
  // CHECK:  [[BCT:%.*]] = "tf.Conv3D"(%arg0, %arg1) <{padding = "SAME", strides = [2, 1, 1, 1, 1]}> : (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>

					Addresses: []api.NodeAddress{{Type: api.NodeExternalIP, Address: "1.1.1.1"}},
				},
			},
			// Columns: Name, Status, Roles, Age, KubeletVersion, NodeInternalIP, NodeExternalIP, OSImage, KernelVersion, ContainerRuntimeVersion
			expected: []metav1.TableRow{
				{
					Cells: []interface{}{"foo1", "Unknown", "<none>", "<unknown>", "", "<none>", "1.1.1.1", "fake-os-image", "<unknown>", "<unknown>"},
				},
			},
		},
		{

    // CHECK-SAME: (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<200x26x26x32xf32>
    %1 = "tf.Conv2DBackpropInput"(%0, %arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<?x?x?x?xf32>
    func.return %1 : tensor<?x?x?x?xf32>
  }

		}}
	}
	complexSubset5 := func(eps *discovery.EndpointSlice) {
		eps.AddressType = discovery.AddressTypeIPv4
		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),
		}}
	}

				Hosts: []string{"foo.bar.com"},
				Port:  &networking.Port{Number: 7, Name: "http", Protocol: "http"},
			},
			"",
		},
		{
			"happy ip",
			&networking.Server{
				Hosts: []string{"1.1.1.1"},
				Port:  &networking.Port{Number: 7, Name: "http", Protocol: "http"},
			},
			"",
		},
		{
			"happy ns/name",
			&networking.Server{
				Hosts: []string{"ns1/foo.bar.com"},

          A map of: output key -> output result.
        """
        out = nn_ops.conv3d(
            input_tensor,
            self.filters,
            strides=[1, 1, 2, 1, 1],
            dilations=[1, 1, 1, 1, 1],
            padding=padding,
            data_format='NDHWC',
        )
        if has_bias:
          out = nn_ops.bias_add(out, self.bias)
        if activation_fn is not None:

		Resolution: model.Passthrough,
	}

	instances := []*model.ServiceInstance{
		{
			Service:     service,
			ServicePort: servicePort,
			Endpoint: &model.IstioEndpoint{
				Address:      "1.1.1.1",
				EndpointPort: 10001,
			},
		},
	}
	inboundFilter := func(c *cluster.Cluster) bool {
		return strings.HasPrefix(c.Name, "inbound|")
	}

	cases := []struct {
		name     string

  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 =