mlir::Operation *op = use.getOwner();
    if (llvm::isa<TF::TensorListGetItemOp>(op) ||
        llvm::isa<TF::TensorListLengthOp>(op) ||
        llvm::isa<TF::TensorListPushBackOp>(op) ||
        llvm::isa<TF::TensorListReserveOp>(op) ||
        llvm::isa<TF::TensorListSetItemOp>(op) ||
        llvm::isa<TF::TensorListStackOp>(op) ||
        llvm::isa<TF::TensorListResizeOp>(op)) {

}

bool IsTPUOp(mlir::Operation* op) {
  return op->hasAttr(TF::kReplicationInfoAttr);
}

StringAttr GetReplicationAttr(mlir::Operation* op) {
  return op->getAttrOfType<StringAttr>(TF::kReplicationInfoAttr);
}

StringAttr GetReplicationAttr(TF::TPUCompilationResultOp op) {
  // Special case for getting the replication region for
  // TPUCompilationResultsOp.
  return op->getAttrOfType<StringAttr>(kTpuCompilationStatus);

        notifications.op(LoadBuildBuildOperationType.Details, [buildPath: ":a:buildSrc"]).parentId == notifications.op(BuildBuildSrcBuildOperationType.Details, [buildPath: ':a']).id

        notifications.op(EvaluateSettingsBuildOperationType.Details, [buildPath: ":"]).parentId == notifications.op(LoadBuildBuildOperationType.Details, [buildPath: ":"]).id

    Operation *op, const ClusteringPolicySet &policies,
    const std::function<bool(Operation *op)> &filter) {
  // Check that op has no side effects. This guarantees that we will not
  // reorder side-effecting ops during cluster formation.
  if (!isMemoryEffectFree(op)) return std::nullopt;

  // Operation rejected by the custom filter.
  if (filter && !filter(op)) return std::nullopt;

            assert op.failure instanceof ProjectConfigurationException
            assert op.failure.message == "A problem occurred configuring <project>."
            assert op.failure.cause.is(failureCause)
        } else {
            assert resultType.isInstance(op.result)
            assert op.failure == null
        }
    }

    List<TestBuildOperationRunner.Log.Record> getOperations() {

  auto walk_result = func_op->walk([&](mlir::Operation* op) {
    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {
      op->setAttr(tensorflow::kReplicationInfoAttr,
                  op->getAttr(TF::kTpuReplicateAttr));
      op->removeAttr(tensorflow::kTpuReplicateAttr);
      op->setAttr(tensorflow::kCompileDeviceTypeAttr,

        op.jvmArguments = null as List

        then:
        op.consumerOperationParameters.jvmArguments == null

        when:
        op.jvmArguments = null as String[]

        then:
        op.consumerOperationParameters.jvmArguments == null

        when:
        op.jvmArguments = []

        then:
        op.consumerOperationParameters.jvmArguments == null

        when:
        op.setJvmArguments()

      operand = rewriter.create<CastOp>(op.getLoc(), tensor_type_f32, operand);
    }
    Value result = rewriter.create<MatMulOp>(
        op.getLoc(), op.getProduct().getType(), operands[0], operands[1],
        op.getTransposeA(), op.getTransposeB());

    rewriter.replaceOp(op, {result});
    return success();
  }
};

// Lowers _UnaryOpsComposition op as a series of original TensorFlow ops that

  bool found = false;
  for (const auto& op : op_list.op()) {
    if (op.name() == "AttributeAccessorsOp") {
      ASSERT_TRUE(op.is_commutative());
      ASSERT_TRUE(op.is_aggregate());
      ASSERT_TRUE(op.allows_uninitialized_input());
      ASSERT_EQ(4, op.deprecation().version());
      ASSERT_EQ(deprecation_msg, op.deprecation().explanation());
      ASSERT_EQ(2, op.attr_size());
      ASSERT_EQ("int", op.attr(0).type());

  double GetOpCost(mlir::Operation* op) const override {
    int64_t count;
    if (ArithmeticCountUtilHelper::GetFirstOutputCount(op, &count))
      return kGPUArithmeticUnitCost * count;
    return kGPUDefaultFixedValuedCost;
  }

  bool IsOpSupported(mlir::Operation* op) const override {
    InferenceType inference_type = GetInferenceType(op);
    if (inference_type != FLOAT) {
      return false;