MLIRContext* context = &getContext();

  for (func::FuncOp func : module.getOps<func::FuncOp>()) {
    llvm::SmallVector<arith::ConstantOp, 4> constant_ops(
        func.getOps<arith::ConstantOp>());
    for (auto op : constant_ops) {
      ShapedType shaped_type =
          RankedTensorType::get({1}, StringType::get(context));

      DenseStringElementsAttr attr;

  const OwningOpRef<mlir::stablehlo::ConstantOp> constant_op =
      builder_.create<mlir::stablehlo::ConstantOp>(
          builder_.getUnknownLoc(), builder_.getI32IntegerAttr(0));
  EXPECT_TRUE(IsStablehloOp(*constant_op));
}

TEST_F(StablehloTypeUtilsTest, IsStablehloOpFailsWithArithOp) {
  const OwningOpRef<mlir::arith::ConstantOp> constant_op =
      builder_.create<mlir::arith::ConstantOp>(builder_.getUnknownLoc(),

  ASSERT_FALSE(module_op->getBodyRegion().empty());
  ASSERT_FALSE(module_op->getBodyRegion().front().empty());

  auto constant_op = dyn_cast_or_null<mlir::stablehlo::ConstantOp>(
      module_op->getBodyRegion().front().front());
  ASSERT_THAT(constant_op, NotNull());

  EXPECT_TRUE(HasRankOf(constant_op, /*rank=*/4));
}

TEST_F(AttrsAndConstraintsTest, HasRankOfReturnsFalseForNonMatchingRank) {

    // while not too large to possibly significantly increase model size.
    if (constant_op.getValue().getNumElements() > 32) continue;
    while (!constant_op.getResult().hasOneUse()) {
      auto new_constant_op = builder.clone(*constant_op.getOperation());
      constant_op.getResult().getUses().begin()->assign(
          dyn_cast<mlir::stablehlo::ConstantOp>(new_constant_op));
    }
  }
}

    quantized_.insert(op);

    if (auto constant_op = dyn_cast<arith::ConstantOp>(op); constant_op) {
      // If the workflow requires inferring ranges from the content
      // (post-training quantization) and it is weight (filter) and hasn't
      // been quantized, we infer the quantization parameters from the content.
      if (infer_tensor_range_ && IsWeight(constant_op) && !IsQuantized(op)) {

class RewriteQuantizedConstantOp
    : public OpRewritePattern<stablehlo::ConstantOp> {
 public:
  using OpRewritePattern<stablehlo::ConstantOp>::OpRewritePattern;

  LogicalResult match(stablehlo::ConstantOp op) const override {
    return success(IsQuantizedTensorType(op.getOutput().getType()));
  }

  void rewrite(stablehlo::ConstantOp op,
               PatternRewriter& rewriter) const override {

  def testBiasedDense(self):
    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]])
    sq = gen_composite_ops.my_biased_dense(t1, t2, t3)
    self.assertAllEqual(sq.numpy().reshape(-1), [-3, 0, 5, 12])

  def testBiasedDenseRelu(self):
    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])

class GraphDecomposeTest(test.TestCase):

  def testAddN(self):
    add = def_function.function(gen_composite_ops.my_add_n)
    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t3 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    sq1 = add([t1])
    sq2 = add([t1, t2])
    sq3 = add([t1, t2, t3])

  ASSERT_TRUE(module_op);

  auto test_func = module_op->lookupSymbol<func::FuncOp>("constant_add");
  ASSERT_THAT(test_func, NotNull());

  auto constant_op =
      FindOperationOfType<mlir::stablehlo::ConstantOp>(test_func);
  EXPECT_TRUE(IsOpQuantizableStableHlo(constant_op));
}

TEST_F(IsOpQuantizableStableHloTest, TerminatorOpNotQuantizable) {
  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kModuleConstantAdd);

      tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
  ])
  def test_float(self, x):
    return constant_op.constant(3.0) * x

  @def_function.function(input_signature=[
      tensor_spec.TensorSpec(shape=(), dtype=dtypes.int32),
  ])
  def test_int(self, x):
    return constant_op.constant(3) * x

  @def_function.function(input_signature=[
      tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),