kXlaHasHostTransferAttrName, true));
    Node* s1 = Sequencer(
        b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1}),
        "F1");

    NodeBuilder node_builder1("F1", "F1", lib_def.get());
    node_builder1.Input(a).Input(b);
    Node* call1 =
        b2.opts().WithControlInputs({s1, b}).FinalizeBuilder(&node_builder1);

    Node* key_constant2 =

	{S1{}, "S0", []int{1}, 0},
	{S1{S0: S0{C: 'c'}}, "C", []int{1, 2}, 'c'},
	{S2{A: 'a'}, "A", []int{0}, 'a'},
	{S2{}, "S1", []int{1}, 0},
	{S2{S1: &S1{B: 'b'}}, "B", []int{1, 0}, 'b'},
	{S2{S1: &S1{S0: S0{C: 'c'}}}, "C", []int{1, 1, 2}, 'c'},
	{S2{}, "D", nil, 0},
	{S3{}, "S1", nil, 0},
	{S3{S2: S2{A: 'a'}}, "A", []int{1, 0}, 'a'},
	{S3{}, "B", nil, 0},
	{S3{D: 'd'}, "D", []int{2}, 0},

func.func @testBroadcastGradientArgs(%s0: tensor<4xi32>, %s1: tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>) {
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>)
  func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
}

// -----

func.func @testBroadcastGradientArgsIncompatibleInputType(%s0: tensor<4xi32>, %s1: tensor<4xi64>) -> (tensor<1xi32>, tensor<0xi32>) {

//===----------------------------------------------------------------------===//

namespace {
// Returns `true` if both s0 & s1 are defined via constant op, and fills
// s0_shape & s1_shape.
bool ExtractInputConstShape(BroadcastGradientArgsOp op,
                            DenseIntElementsAttr& s0, DenseIntElementsAttr& s1,
                            SmallVectorImpl<int64_t>& s0_shape,
                            SmallVectorImpl<int64_t>& s1_shape) {

type nodeConditionList []v1.NodeConditionType

// Equal adds the ability to check equality between two lists of node conditions.
func (s1 nodeConditionList) Equal(s2 nodeConditionList) bool {
	if len(s1) != len(s2) {
		return false
	}
	for _, item := range s1 {
		if !hasNodeCondition(s2, item) {
			return false
		}
	}
	return true
}

var _ framework.ScorePlugin = &TestScorePlugin{}

var cmpOpts = []cmp.Option{
	cmp.Comparer(func(s1 *framework.Status, s2 *framework.Status) bool {
		if s1 == nil || s2 == nil {
			return s1.IsSuccess() && s2.IsSuccess()
		}
		return s1.Code() == s2.Code() && s1.Plugin() == s2.Plugin() && s1.Message() == s2.Message()
	}),
}

array with the operand.
</p>

<pre>
var a [10]int
s1 := a[3:7]   // underlying array of s1 is array a; &amp;s1[2] == &amp;a[5]
s2 := s1[1:4]  // underlying array of s2 is underlying array of s1 which is array a; &amp;s2[1] == &amp;a[5]
s2[1] = 42     // s2[1] == s1[2] == a[5] == 42; they all refer to the same underlying array element
</pre>


<h4>Full slice expressions</h4>

                                shape_inference::ShapeHandle s1) -> bool {
    if (s0.SameHandle(s1) || (!c->RankKnown(s0) && !c->RankKnown(s1))) {
      return true;
    }
    if (c->Rank(s0) != c->Rank(s1)) {
      return false;
    }
    for (int i = 0; i < c->Rank(s0); ++i) {
      if (!c->Dim(s0, i).SameHandle(c->Dim(s1, i))) {
        int64_t val0 = c->Value(c->Dim(s0, i));

			},
		},
	}

	cmpOpts := []cmp.Option{
		cmp.Comparer(func(s1 framework.Status, s2 framework.Status) bool {
			return s1.Code() == s2.Code() && reflect.DeepEqual(s1.Reasons(), s2.Reasons())
		}),
	}

	for _, tt := range tests {
		t.Run(tt.name, func(t *testing.T) {
			_, ctx := ktesting.NewTestContext(t)

    Pure]> {
  let summary = "Return the shape of s0 op s1 with broadcast.";

  let description = [{
Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the
broadcasted shape. `s0`, `s1` and `r0` are all integer vectors.
  }];

  let arguments = (ins
    TFL_I32OrI64Tensor:$s0,
    TFL_I32OrI64Tensor:$s1
  );

  let results = (outs
    TFL_I32OrI64Tensor:$r0
  );