code: def _register_layer_attributes(self):
      # Add RNN-specific recurrent_reuse_factor attribute
      rnn_layers = [
          SimpleRNN,
          LSTM,
          GRU,
      ]
  
      for layer in rnn_layers:
          attrs = self.attribute_map.get(layer, [])
          attrs.append(
              ConfigurableAttribute("recurrent_reuse_factor", default=1)
          )
          attrs.append(
              ConfigurableAttribute("table_size", default=1024)
          )
          attrs.append(
              TypeAttribute("table", default=FixedPrecisionType(18, 8))
          )
          self.attribute_map[layer] = attrs
  
imports: import os
  from contextlib import contextmanager
  import numpy
  from hls4ml.backends import FPGABackend
  from hls4ml.model.attributes import ConfigurableAttribute, TypeAttribute
  from hls4ml.model.flow import register_flow
  from hls4ml.model.layers import GRU, LSTM, Activation, Conv1D, Conv2D, Dense, Embedding, Layer, SimpleRNN, Softmax
  from hls4ml.model.optimizer import get_backend_passes, layer_optimizer
  from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType
  from hls4ml.report import parse_quartus_report
label_desc: Use image node_id 2 for calling the QuartusBackend obj's underlying member method code with example usage: obj._register_layer_attributes() without return types