task producer {
                def inputTxt = file("input.txt")
                def outputTxt = file("build/output.txt")
                inputs.files(inputTxt)
                outputs.file(outputTxt)
                doLast {
                    outputTxt.text = inputTxt.text
                }
            }
        """

        file("input.txt").text = "input"
        def outputFile = file("build/output.txt")

//   was called with fallback to the non-MLIR Bridge. This is just for logging
//   purposes and doesn't affect logic. module_name - What the input module name
//   is for debugging help.
//
// Output: Modifies the input module in place with clustered operations.
//   status - Whether the transformation to cluster the input MLIR module was
//   successful.
tensorflow::Status RunFunctionTf2xlaClusteringBridge(

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-xla-validate-inputs

// expected-error @+1 {{expects no nested calls of entry functions as they prevent graph traversal in some passes from working correctly}}
func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {

	}
	for _, tt := range tests {
		t.Run(tt.name, func(t *testing.T) {
			got := &bytes.Buffer{}
			sw := XdsStatusWriter{Writer: got}
			input := map[string]*discovery.DiscoveryResponse{}
			for key, ss := range tt.input {
				input[key] = ss
			}

			err := sw.PrintAll(input)
			if tt.wantErr {
				assert.Error(t, err)
			} else {
				assert.NoError(t, err)
			}
			want, _ := os.ReadFile(tt.want)

}
}

//CHECK: module attributes
//CHECK-SAME: keep_stablehlo_constant = "true"
//CHECK-NEXT:  func.func @main(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> attributes {tf.entry_function = {inputs = "arg0", outputs = "vhlo.dynamic_update_slice_v1"}} {
//CHECK-DAG:    %[[c0:.+]] = stablehlo.constant dense<2.000000e+00> : tensor<1x1x2xf32>
//CHECK-DAG:    %[[c1:.+]] = stablehlo.constant dense<1> : tensor<i32>

                continue;
            }
            File outputLocation = report.getOutputLocation().get().getAsFile();
            inputs.add(new ReportState(report.getDisplayName(), outputLocation, outputLocation.exists()));
        }
        return inputs;
    }

    private Set<Report> getEnabledInputReports() {
        HashSet<Reporting<? extends ReportContainer<?>>> allAggregatedReports = Sets.newHashSet(aggregated);

	"k8s.io/apiserver/pkg/admission/plugin/cel"
	"k8s.io/apiserver/pkg/authorization/authorizer"
)

var _ cel.ExpressionAccessor = &ValidationCondition{}

// ValidationCondition contains the inputs needed to compile, evaluate and validate a cel expression
type ValidationCondition struct {
	Expression string
	Message    string
	Reason     *metav1.StatusReason
}

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s


module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1660 : i32}} {
  func.func @main(%arg0: tensor<2x3xi32>) -> tensor<2x3xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "args_tf_0", outputs = "Identity"}} {
    %0 = tf_executor.graph {

    RunOptions run_options;

    Tensor input_a = CreateInputTensor(shape, 0);
    Tensor input_b = CreateInputTensor(shape, shape.num_elements());
    Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements());
    TF_RETURN_IF_ERROR(session->Run(
        run_options,
        {std::make_pair("a", input_a), std::make_pair("b", input_b),
         std::make_pair("c", input_c)},
        {"m"}, {}, &golden_output_tensors, nullptr));

                outputs.cacheIf { true }

                inputs.files(inputFile)
                    .withPropertyName("dummy-input")
                    .withPathSensitivity(PathSensitivity.RELATIVE)

                outputs.dir(outputDir)
                    .withPropertyName('dummy-output')

                doLast {
                    outputFile.text = inputFile.text
                }
            }