tests := []struct {
		name       string
		pod        *v1.Pod
		node       *v1.Node
		wantStatus *framework.Status
	}{
		{
			name: "A pod having no tolerations can't be scheduled onto a node with nonempty taints",
			pod:  podWithTolerations("pod1", []v1.Toleration{}),
			node: nodeWithTaints("nodeA", []v1.Taint{{Key: "dedicated", Value: "user1", Effect: "NoSchedule"}}),

	tests := []struct {
		name           string
		wantOutputFile string
		callPrime      bool
		wantErr        bool
	}{
		{
			name:           "returns expected bootstrap dump from Envoy onto Stdout",
			callPrime:      true,
			wantOutputFile: "testdata/bootstrapdump.json",
		},
		{
			name:    "errors if config dump is not primed",
			wantErr: true,
		},
	}
	for _, tt := range tests {

    return %cst_0 : tensor<8xf32>
  }
}

// -----

// Tests the case when there is no ConstOp.
module attributes {tf_saved_model.semantics} {

// Check that nothing happens when there's no ConstOp in the graph.
// CHECK-NOT: "tf_saved_model.session_initializer"()

  ASSERT_THAT(test_func, NotNull());

  Operation* mul_op = FindOperationOfType<TF::MulOp>(test_func);
  SmallVector<Value> results = ConstantFoldOpIfPossible(mul_op);
  EXPECT_THAT(results, SizeIs(1));
  EXPECT_TRUE(isa<TF::ConstOp>(results[0].getDefiningOp()));
}

TEST_F(ConstantFoldingTest, NotFoldingIdentity) {
  constexpr absl::string_view kModuleCode = R"mlir(
    module {
      func.func @test_fold_constant() -> (tensor<1024x24x24x3xf32>) {

      auto qcast = b.create<quantfork::QuantizeCastOp>(
          q.getLoc(), q.getOutput().getType(), q.getInput());
      q.getOutput().replaceAllUsesWith(qcast);
      q.erase();
    } else if (auto q = llvm::dyn_cast<ConstOp>(op)) {
      auto value = q.getValue();
      auto type = q.getResult().getType();
      if (arith::ConstantOp::isBuildableWith(value, type)) {
        auto c = b.create<arith::ConstantOp>(q.getLoc(), q.getValue());

          tensorflow::io::JoinPath(saved_model_dir, asset_filename);
      ShapedType shaped_type =
          RankedTensorType::get({1}, TF::StringType::get(builder.getContext()));
      auto const_op = builder.create<TF::ConstOp>(
          asset.getLoc(),
          DenseStringElementsAttr::get(shaped_type, {filename}));
      for (auto init_op : init_table_from_text_file_ops_to_erase) {

  TF_EXPECT_OK(NodeDefBuilder("identity_op", "Identity")
                   .Input(FakeInput(DT_FLOAT))
                   .Attr("T", DT_FLOAT)
                   .Finalize(node_def()));
  TF_EXPECT_OK(InitOp());
  AddInputFromArray<float>(TensorShape({1, 2}), {6.9, 4.2});
  TF_EXPECT_OK(RunOpKernel());

  XlaCompiler::SingleOpCompileArgument single_op_arg(*context_);

  std::vector<XlaArgument> args(1);

		wantErr            bool
	}{
		{
			name:             "returns expected secret summary onto Stdout",
			callPrime:        true,
			wantOutputSecret: "testdata/secretsummary.txt",
		},
		{
			name:    "errors if config dump is not primed",
			wantErr: true,
		},
		{
			name:               "returns expected workload summary onto Stdout",
			callPrime:          true,
			wantOutputWorkload: "testdata/workloadsummary.txt",
		},

  // CHECK-NEXT: [[PREFIX:%.*]] = tf_mlrt.constop
  %cst = "tf.Const"() {__op_key = 0: i32, value = dense<"restore_ariables"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  // CHECK-NEXT: [[SLICE:%.*]] = tf_mlrt.constop
  %cst_0 = "tf.Const"()  {__op_key = 1: i32, value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  // CHECK-NEXT: [[NAME:%.*]] = tf_mlrt.constop

	// Push K elements randomly onto the stacks.
	sum := 0
	for i := 0; i < K; i++ {
		sum += i
		node := allocMyNode(i)
		LFStackPush(stacks[i%2], fromMyNode(node))
	}
	c := make(chan bool, P)
	for p := 0; p < P; p++ {
		go func() {
			r := rand.New(rand.NewSource(rand.Int63()))
			// Pop a node from a random stack, then push it onto a random stack.
			for i := 0; i < N; i++ {