// input and the outputs are all using this type and predicate is tensor<i1>
  // type).
  if (mlir::isa<FunctionType>(types.front())) {
    FunctionType type = mlir::cast<FunctionType>(types.front());
    if (type.getNumInputs() < 2)
      return parser.emitError(parser.getNameLoc())
             << " expects a single data type and a predicate";
    result.types.assign(type.getResults().begin(), type.getResults().end());

  builder.create<func::ReturnOp>(func.getLoc(), op.getResults());
  return success();
}

LogicalResult VerifySgnnProjection(func::FuncOp func, FuncAttr attr) {
  if (func.getFunctionType().getNumInputs() != 2 ||
      func.getFunctionType().getNumResults() != 1) {
    return func.emitError() << "Mismatched number of inputs and outputs.";
  }
  auto values_type = GetInputType(func, 0);

                         func::FuncOp fn, OpBuilder* builder) {
  FunctionType fn_type = fn.getFunctionType();
  llvm::SmallVector<Value, 4> operands;
  int num_operands = fn_type.getNumInputs();
  operands.reserve(num_operands);
  for (int i = 0; i < num_operands; ++i) {
    Value val = get_arg(i);
    Type expected = fn_type.getInput(i);
    if (val.getType() != expected) {
      val =

      module.lookupSymbol<mlir::func::FuncOp>("main");
  TF_RET_CHECK(main_func != nullptr) << "No main function found";
  mlir::FunctionType func_type = main_func.getFunctionType();

  int num_args = func_type.getNumInputs();
  xla_input_shapes->reserve(num_args);

  std::vector<xla::Shape> individual_arg_shapes;
  individual_arg_shapes.reserve(num_args);
  for (int i = 0; i < num_args; ++i) {