}
}


// -----
//  Decomposable Resource Ops usage
//
// CHECK-LABEL:  func.func @serving_default
// CHECK:      "tf.VarHandleOp"
// CHECK-NEXT: "tf.IfrtLoadVariable"
// CHECK-NEXT: "tf.GatherV2"
// CHECK-NEXT:  return 
//
module {
  func.func @serving_default() -> tensor<1x3xbf16> {
    %cst = "tf.Const"() <{value = dense<[1]> : tensor<1xi32>}> : () -> tensor<1xi32>

}

func.func @gatherV2VectorIndices(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> {
  %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x3x5x20xf32>
  func.return %1 : tensor<1x3x5x20xf32>

// CHECK-LABEL:gatherV2VectorIndices

  );

  TF_DerivedOperandTypeAttr Tindices = TF_DerivedOperandTypeAttr<1>;
  TF_DerivedOperandTypeAttr Tparams = TF_DerivedOperandTypeAttr<0>;
}

def TF_GatherV2Op : TF_Op<"GatherV2", [Pure]> {
  let summary = [{
Gather slices from `params` axis `axis` according to `indices`.
  }];

  let description = [{
`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).