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)) {

  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);

#define TENSORFLOW_COMPILER_JIT_XLA_DEVICE_OPS_H_

#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/kernels/constant_op.h"
#include "tensorflow/core/kernels/data/finalize_dataset_op.h"
#include "tensorflow/core/kernels/data/generator_dataset_op.h"
#include "tensorflow/core/kernels/data/iterator_ops.h"

      // Create new ConstantOp-ConvertOp-Operation sequences. At this moment,
      // old ConstantOp is guaranteed to have one F32->F16 convert op regardless
      // of its number of users.
      rewriter.setInsertionPointAfter(op);
      // create new F16 constant op in that location
      ConstantOp new_const = rewriter.create<ConstantOp>(
          op->getLoc(), new_result_type, new_value_attr);

        changed = true;
      }
    }
    return changed;
  }

  // Convert ConstantOp-CastOp-Operation sequence into new ConstantOp
  // -Dequantize-Operation where the new ConstantOp has float16 data type.
  bool convertToFloat16Constant(PatternRewriter& rewriter,
                                arith::ConstantOp op) const {
    for (auto connected_op : op.getResult().getUsers()) {

class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> {
 public:
  explicit PrepareDRQQuantizableOp(MLIRContext* context,
                                   const quant::QuantizationSpecs& quant_specs,
                                   OpSet op_set,
                                   bool enable_per_channel_quantization)
      : OpRewritePattern<arith::ConstantOp>(context),
        quant_specs_(quant_specs),

template <typename Op, typename ElementType = Type, typename ValType,
          typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());
  auto rhs = dyn_cast_or_null<DenseElementsAttr>(rhs_op.getValue());

        llvm::isa<BlockArgument>(tpose_arg2.getPerm())) {
      return failure();
    }
    auto perm1 = llvm::dyn_cast_or_null<arith::ConstantOp>(
        tpose_arg1.getPerm().getDefiningOp());
    auto perm2 = llvm::dyn_cast_or_null<arith::ConstantOp>(
        tpose_arg2.getPerm().getDefiningOp());
    if (!perm1 || !perm2) {
      return failure();
    }