c.Assert(response.StatusCode, http.StatusOK)

	var results ListBucketsResponse
	// parse the list bucket response.
	decoder := xml.NewDecoder(response.Body)
	err = decoder.Decode(&results)
	// validating that the xml-decoding/parsing was successful.
	c.Assert(err, nil)

	// Fetch the bucket created above
	var createdBucket Bucket
	for _, b := range results.Buckets.Buckets {
		if b.Name == bucketName {

    func.return
  }
  func.func @empty_func() {
    func.return
  }
}

// -----

// Tests collecting compilation and execution devices results in an error.

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  func.func @bad_devices() {

				},
			},
			err: nil,
			deletableResources: map[schema.GroupVersionResource]struct{}{
				{Group: "apps", Version: "v1", Resource: "pods"}: {},
			},
		},
		"nonspecific failure, includes usable results": {
			serverResources: []*metav1.APIResourceList{
				{
					GroupVersion: "apps/v1",
					APIResources: []metav1.APIResource{

// same memory state can reuse the results of the first call.
// See issue 61725.
// Note that this could pretty easily generalize to any pure function.
(SelectN [0] (StaticLECall {f} x y (SelectN [1] c:(StaticLECall {g} x y mem))))
  && isSameCall(f, "runtime.cmpstring")
  && isSameCall(g, "runtime.cmpstring")
=> @c.Block (SelectN [0] <typ.Int> c)

// If we don't use the result of cmpstring, might as well not call it.

// CHECK:         return %[[VAL_128]], %[[VAL_129]] : tensor<1x10xi32>, tensor<i32>
// CHECK:       }
}

// -----

module {
// expected-warning @+1 {{Invalid number of results from non_max_suppression_padded_v2}}

			// testing whether the copy was successful.
			var results ListPartsInfo
			results, err = obj.ListObjectParts(context.Background(), testCase.bucketName, testObject, testCase.uploadID, 0, 1, ObjectOptions{})
			if err != nil {
				t.Fatalf("Test %d: %s: Failed to look for copied object part: <ERROR> %s", i+1, instanceType, err)
			}
			if len(results.Parts) != 1 {

   %outputs_2 = "tf.TensorSliceDataset"(%outputs_0) {device = "", output_shapes = [#tf_type.shape<>], metadata=""} : (tensor<*xf32>) -> tensor<!tf_type.variant>
   func.return
  }

  // Test resource result subtypes are propagated to call op results.
  // CHECK-LABEL: func @pcall_resource_result
  func.func @pcall_resource_result(%arg0: tensor<*x!tf_type.resource<tensor<f32>>>) {
    // CHECK: "tf.StatefulPartitionedCall"

// Hence the second HPA controller reconcile cycle might return different result (comparing with the first run).
// Current test infrastructure has a race condition, when several reconcile cycles will be performed
//    while it should be stopped right after the first one. And the second will raise an exception
//    because of different result.

			otherPods = append(otherPods, op)
		}
		attrs.OtherPods = otherPods
	}
	for _, podAdmitHandler := range kl.admitHandlers {
		if result := podAdmitHandler.Admit(attrs); !result.Admit {
			return false, result.Reason, result.Message
		}
	}

	return true, "", ""
}

func (kl *Kubelet) canRunPod(pod *v1.Pod) lifecycle.PodAdmitResult {

  func.return %0, %1 : tensor<*xi32>, tensor<*xf32>
}

func.func @main(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<i32> {
  // expected-error @+1 {{number of operands does not match number of results}}
  %0:1 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1 = func.call @WhileOp_cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>