// CHECK: %[[dequantize:.*]] = "tf.PartitionedCall"(%[[maxpool]]
// CHECK-SAME: f = @dequantize_i8
// CHECK: return %[[dequantize]]

// CHECK: -------- Quantization Summary --------
// CHECK: Number of quantized layers in the model
// CHECK: --------------------------------
// CHECK: Name    Count/Total

using ::tensorflow::quantization::OpSet;

constexpr absl::string_view kQuantizeCompositeFunctionsStepName =
    "_quantize_composite_functions";
constexpr StringRef kQuantizeFuncName = "quantize_i8";
constexpr StringRef kDequantizeFuncName = "dequantize_i8";
constexpr StringRef kAttrMapAttribute = "attr_map";
constexpr StringRef kQuantizedOpsAttribute = "tf_quant.quantized_ops";
constexpr StringRef kCompositeFuncPrefix = "composite_";

    %round = "tf.Round"(%clamp_min) : (tensor<*xf32>) -> tensor<*xf32>
    %i8 = "tf.Cast"(%round) : (tensor<*xf32>) -> tensor<*xi8>
    func.return %i8 : tensor<*xi8>
  }

  func.func @dequantize_i8(%input : tensor<*xi8>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xf32> {
    // Use identity op to avoid the weight being constant-folded.
    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>

      : OpRewritePattern<DequantizeOp>(context) {}

  LogicalResult matchAndRewrite(DequantizeOp dequantize_op,
                                PatternRewriter& rewriter) const override {
    if (!dequantize_op->hasOneUse()) return failure();

    auto use = dequantize_op->use_begin();
    Operation* passthrough_op = use->getOwner();
    unsigned operand_index = use->getOperandNumber();

        output_type.print(llvm::errs() << "Requested output type ");
        dequantize_op.emitError(" Couldn't be modified to the requested type.");
        return failure();
      }
      new_output_types[i] = returned_type;
      terminator->setOperand(i, returned_value);
      if (dequantize_op.use_empty()) {
        dequantize_op.erase();
      }
    }
  }
  return success();
}

  quant_spec.set_tensor_type(QuantizationComponentSpec::TENSORTYPE_INT_8);

  std::optional<TF::PartitionedCallOp> dequantize_op = ApplyUniformQuantization(
      pattern_rewriter, cast<TF::ConstOp>(value.getDefiningOp()), quant_spec);
  EXPECT_TRUE(dequantize_op.has_value());
  EXPECT_EQ(dequantize_op.value().func().getName().str(),
            "composite_dequantize_uniform");
}

}  // namespace

      : OpRewritePattern<RootOpT>(context, /*benefit=*/300) {}

 private:
  // Collects all candidate ops for quantization, which are the
  // `dequantize_op`'s users.
  FailureOr<SmallVector<Operation*>> CollectCandidateOps(
      DequantizeOpT dequantize_op) const {
    auto users = dequantize_op->getResult(0).getUsers();
    return SmallVector<Operation*>(users.begin(), users.end());
  }

            mlir::quant::QuantizedType::castToExpressedType(input_type);
        builder->setInsertionPoint(op);
        auto dequantize_op = builder->create<TFL::DequantizeOp>(
            op->getLoc(), dequantized_input_type, input.get());
        dequantized_inputs.push_back(dequantize_op);
      } else {
        dequantized_inputs.push_back(input.get());
      }
    }

    // Result types.

  auto quantize_op = FindOperationOfType<quantfork::QuantizeCastOp>(test_func);
  EXPECT_FALSE(IsOpQuantizableStableHlo(quantize_op));

  auto dequantize_op =
      FindOperationOfType<quantfork::DequantizeCastOp>(test_func);
  EXPECT_FALSE(IsOpQuantizableStableHlo(dequantize_op));
}

TEST_F(IsOpQuantizableStableHloTest,
       XlaCallModuleOpQuantizableWhenNotDenylisted) {

    Operation* returned_op = returned_value.getDefiningOp();
    if (returned_op && returned_op->hasOneUse() &&
        llvm::isa<DequantizeOp>(returned_op)) {
      auto dequantize_op = llvm::cast<DequantizeOp>(returned_op);
      Value dequantized_result = dequantize_op.getInput();
      output_types.push_back(dequantized_result.getType());
      terminator->setOperand(i, dequantized_result);
      returned_op->erase();
    } else {