namespace mlir {

namespace {

constexpr char kDeviceAttr[] = "device";

#define GEN_PASS_DEF_RESOURCEOPLIFTINGPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

// Lift resource operations out of device computation.
struct ResourceOpLiftingPass
    : public impl::ResourceOpLiftingPassBase<ResourceOpLiftingPass> {
  void runOnOperation() override;
};

bool IsResource(Value value) {

  func.return %read : tensor<f32>
}

// -----

// Tests that the pass reports error on non-aliasing WhileRegion input/output
// resources. It cannot lift resource ops from such WhileRegion ops and should
// fail with a helpful error message.

func.func @fail_non_aliasing_resource_input_output() -> () {

              edge->src()->name(), ":", edge->src_output());
        }
      }

      Subgraph& dst_subgraph = subgraphs_[dst_func_id];
      // Ignore control edges entering the subgraph. We will lift them onto
      // the enclosing call operators in BuildOutputGraph().
      if (!edge->IsControlEdge()) {
        TF_RETURN_IF_ERROR(
            dst_subgraph.RecordArg(edge, node_images, src_arg_pairs));
      }

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...

				"application/vnd.kinar",
				"application/vnd.koan",
				"application/vnd.kodak-descriptor",
				"application/vnd.liberty-request+xml",
				"application/vnd.llamagraphics.life-balance.desktop",
				"application/vnd.llamagraphics.life-balance.exchange+xml",
				"application/vnd.lotus-1-2-3",
				"application/vnd.lotus-approach",
				"application/vnd.lotus-freelance",
				"application/vnd.lotus-notes",

     *
     * <p>All objects <b>MUST</b> expose their name as a bean property named "name". The name must be constant for the life of the object.</p>
     *
     * @param type The type of objects for the container to contain.
     * @param <T> The type of objects for the container to contain.
     * @return The container.
     */

		makeInstance(tcpStatic, "2.2.2.2", 444, tcpStatic.Spec.(*networking.ServiceEntry).Ports[0], nil, MTLS),
	}, "2.2.2.2")
}

// Keeping this test for legacy - but it never happens in real life.
func TestServiceDiscoveryInstances(t *testing.T) {
	store, sd, _ := initServiceDiscovery(t)

	createConfigs([]*config.Config{httpDNS, tcpStatic}, store, t)

	}, {
		requestVersion:    flowcontrolv1.SchemeGroupVersion,
		allowZero:         true,
		concurrencyShares: 1,
		errExpected:       nil,
	}, {
		// this should never really happen in real life, the request
		// context should always contain the request {group, version}
		requestVersion:    schema.GroupVersion{},
		concurrencyShares: 0,

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-lift-quantizable-spots-as-functions | FileCheck %s

// CHECK-LABEL: @conv_fn(
// CHECK-SAME:          %[[ARG_0:.*]]: tensor<1x3x3x4xf32>
func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>

        /*Size=*/filter_scale_values.size(), /*Value=*/0);

    // Use quantization dimension = 3 that corresponds to the output channel
    // dimension, assuming the filter format is `[0, 1, i, o]`.
    // TODO: b/291029962 - Lift the assumption above and retrieve the
    // quantization dimension from the `dimension_numbers` attribute.
    UniformQuantizedPerAxisType filter_quantized_element_type =
        CreateI8F32UniformQuantizedPerAxisType(