// TFL lstm only supports time-majored inputs, so if it's not time-majored,
  // we will transpose the inputs and outputs.
  auto time_major_attr = func_op->getAttrOfType<BoolAttr>("tf.time_major");
  if (time_major_attr == nullptr) return failure();

  bool time_majored = time_major_attr.getValue();
  auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
  if (!input_type) {

    // from inferring output shape for Merge node (we need shapes for all its
    // inputs).
    // For loop invariant resource input's Merge node, we set output resource
    // shape as Enter node's resource shape.
    // TODO(b/129367850): clean this up.
    if (n->IsMerge() && n->output_type(0) == DT_RESOURCE) {
      // Check if this is a loop invariant input's Merge node. We do it by

  std::vector<NodeDefBuilder::NodeOut> inputs;
  inputs.emplace_back("min", 0, DT_FLOAT);
  inputs.emplace_back("max", 0, DT_FLOAT);
  inputs.emplace_back("histogram", 0, DT_INT64);
  inputs.emplace_back("min", 0, DT_FLOAT);
  inputs.emplace_back("max", 0, DT_FLOAT);
  inputs.emplace_back("histogram", 0, DT_INT64);

  const std::string dir = testing::TmpDir();

	t.Cleanup(func() {
		t.Helper()
		types := slices.Map(mc.inputs, func(e any) string {
			return fmt.Sprintf("%T", e)
		})
		assert.Equal(t, len(mc.inputs), 0, fmt.Sprintf("some inputs were not consumed: %v (%v)", mc.inputs, types))
	})
	return mc
}

func GetMockCollection[T any](mc *MockCollection) krt.Collection[T] {
	return krt.NewStaticCollection(extractType[T](&mc.inputs))
}

    static class ExpectingSome extends InputsSpec {

        final List<Matcher<String>> inputs

        ExpectingSome(List<Matcher<String>> inputs = []) {
            this.inputs = inputs
        }

        @Override
        InputsSpec expect(Matcher<String> input) {
            inputs.add(input)
            this
        }

        @Override
        InputsSpec expectNone() {

		reg: regInfo{
			inputs: []inputInfo{
				{0, 65280}, // X0 X1 X2 X3 X4 X5 X6 X7
				{1, 65280}, // X0 X1 X2 X3 X4 X5 X6 X7
			},
			outputs: []outputInfo{
				{0, 65280}, // X0 X1 X2 X3 X4 X5 X6 X7
			},
		},
	},
	{
		name:         "SUBSD",
		argLen:       2,
		resultInArg0: true,
		asm:          x86.ASUBSD,
		reg: regInfo{
			inputs: []inputInfo{

}

// CHECK-LABEL: func @tensor_array_while_test
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) {
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)

  // Condition function that returns input > 0.
  CondGraphBuilderFn cond_fn = [](const Scope& scope,
                                  const std::vector<Output>& inputs,
                                  Output* output) {
    DCHECK_EQ(inputs.size(), 1);
    *output = ops::Greater(scope, inputs[0], 0);
    return scope.status();
  };

  // Body function that subtracts one from input.

              NodeWith(Op("Add"), Name("add"),
                       Inputs(Out(NodeWith(Name("placeholder_a"))),
                              Out(NodeWith(Name("placeholder_b"))))));

  EXPECT_EQ(Explain(add.node(), NodeWith(Inputs())),
            "\nexpected 0 inputs but node has 2");
  EXPECT_EQ(
      Explain(add.node(), NodeWith(Inputs(Out(NodeWith(Name("blah"))), _))),

	for i = 0; i < 8; i++ {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {