output_zero_point_value);

    Value conv_output_value = op.getResult();
    auto output_uniform_quantized_tensor_type = RankedTensorType::getChecked(
        rewriter.getUnknownLoc(),
        /*shape=*/
        mlir::cast<TensorType>(conv_output_value.getType()).getShape(),
        output_uniform_quantized_type);

    SmallVector<Type> new_conv_output_types = {

    const auto func_type =
        builder.getFunctionType(/*inputs=*/{}, /*results=*/{});
    auto summary_func = builder.create<func::FuncOp>(
        builder.getUnknownLoc(), /*sym_name=*/"summary", func_type);
    summary_func.setPrivate();
    summary_func->setAttr("quantization_summary",
                          builder.getStringAttr(log_message));
  }

 private:

        min_maxs);
    // TODO(fengliuai): this quantization dimension isn't correct.
    axis = b.getI64IntegerAttr(tensor.quantization->quantized_dimension);
  }
  return b.create<mlir::quantfork::StatisticsOp>(b.getUnknownLoc(), res,
                                                 layer_stats, axis_stats, axis);
}

// Returns true if this is a basic LSTM op.
bool IsBasicLSTMOp(tflite::BuiltinOptionsUnion op_union) {