// CHECK:        ^bb0(%[[ARG_4:.*]]: tensor<i1>, %[[ARG_5:.*]]: tensor<i64>)
    // CHECK:          %[[GET_NEXT:[0-9]*]] = "tf.IteratorGetNextAsOptional"(%[[ANON_ITER]])
    // CHECK-NEXT:     %[[HAS_VALUE:[0-9]*]] = "tf.OptionalHasValue"(%[[GET_NEXT]])
    // CHECK-NEXT:     %[[IF:.*]] = "tf.IfRegion"(%[[HAS_VALUE]])
    // CHECK-NEXT:       %[[GET_VALUE:[0-9]*]] = "tf.OptionalGetValue"(%[[GET_NEXT]])

  // their "no-op" expansion is known to cause problems in some cases.
  static const char* kOpsToSkip[] = {
      "IdentityOp",
      "NoOp",              // b/174596063
      "OptionalHasValue",  // b/173136483
      "OptionalGetValue",  // b/173136483
      "VarHandleOp",       // b/176819198
  };
  for (const char* skip : kOpsToSkip) {

                          data::OptionalFromValueOp);                          \
  REGISTER_KERNEL_BUILDER(                                                     \
      Name("OptionalHasValue").Device(DEVICE).HostMemory("has_value"),         \
      data::OptionalHasValueOp);                                               \
  REGISTER_KERNEL_BUILDER(Name("OptionalGetValue").Device(DEVICE),             \

// Moves TPUCompileMlir ops as far to the front as possible.
std::unique_ptr<OperationPass<func::FuncOp>> CreateMoveTpuCompileToFrontPass();

// Decomposes OptionalFromValue, OptionalGetValue, OptionalNone,
// and OptionalHasValue
std::unique_ptr<OperationPass<ModuleOp>> CreateDecomposeOptionalsPass();

//===----------------------------------------------------------------------===//
// XlaCallModule

        %2 = "tf.IteratorGetNextAsOptional"(%0) {output_shapes = [#tf_type.shape<32>], output_types = [f32]} : (tensor<!tf_type.resource>) -> tensor<!tf_type.variant>
        %3 = "tf.OptionalHasValue"(%2) : (tensor<!tf_type.variant>) -> tensor<i1>
        %4 = "tf.IfRegion"(%3) ({
          %5 = "tf.OptionalGetValue"(%2) : (tensor<!tf_type.variant>) -> tensor<32xf32>

  ^bb0(%arg2: tensor<i1>, %arg3: tensor<i64>):
    %2 = "tf.IteratorGetNextAsOptional"(%0) {output_shapes = [#tf_type.shape<32>], output_types = [f32]} : (tensor<!tf_type.resource>) -> tensor<!tf_type.variant>
    %3 = "tf.OptionalHasValue"(%2) : (tensor<!tf_type.variant>) -> tensor<i1>
    %4 = "tf.IfRegion"(%3) ({
      %5 = "tf.OptionalGetValue"(%2) : (tensor<!tf_type.variant>) -> tensor<32xf32>

  TF_DerivedResultShapeListAttr output_shapes = TF_DerivedResultShapeListAttr<0>;
  TF_DerivedResultTypeListAttr output_types = TF_DerivedResultTypeListAttr<0>;
}

def TF_OptionalHasValueOp : TF_Op<"OptionalHasValue", [Pure]> {
  let summary = [{
Returns true if and only if the given Optional variant has a value.
  }];

  let arguments = (ins
    TF_VariantTensor:$optional
  );