return %0 : tensor<1x4x4x3xf32>
    }
  }
)mlir";

// TOOD: b/323478683 - Directly use types rather than creating a `unique_ptr`.
std::unique_ptr<quant::OpQuantSpec> GetOpQuantSpec(
    const mlir::Operation* op,
    bool disable_per_channel_for_dense_layers = false) {
  auto spec = std::make_unique<quant::OpQuantSpec>();
  spec->coeff_op_quant_dim[1] = 3;

namespace mlir {
namespace TFL {
// Includs an auto-generated function, which can retrieve the quantization
// specification for an TFL operation. The signature of the function is
//   std::unique_pointer<OpQuantSpec> TFL::GetOpQuantSpec(Operation *)
#include "tensorflow/compiler/mlir/lite/utils/generated_op_quant_spec_getters.inc"

namespace {

#define GEN_PASS_DEF_DEFAULTQUANTPARAMSPASS

  // name.
  std::vector<Record *> defs = records.getAllDerivedDefinitions("Op");
  llvm::sort(defs, LessRecord());

  OUT(0) << "static std::unique_ptr<quant::OpQuantSpec> "
            "GetOpQuantSpec(mlir::Operation *op, bool "
            "disable_per_channel_for_dense_layers = false) {\n";
  // TODO(b/176258587): Move to OpTrait if this should be generalized.
  // Add special handling for LSTM.

}

#include "tensorflow/compiler/mlir/lite/utils/generated_op_quant_spec_getters.inc"

bool PrepareQuantizePass::RemoveRedundantStats(func::FuncOp func) {
  return RemoveRedundantStatsOps(
      func, std::bind(GetOpQuantSpec, std::placeholders::_1,
                      quant_specs_.disable_per_channel_for_dense_layers));
}

static Value Quantized(Operation* user) {