code: def _register_layer_attributes(self):
      # Add RNN-specific attributes, recurrent_reuse_factor and static implementation
      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(
                  "static", value_type=bool, default=True
              )
          )
          attrs.append(
              ConfigurableAttribute("table_size", default=1024)
          )
          attrs.append(
              TypeAttribute("table", default=FixedPrecisionType(18, 8))
          )
          self.attribute_map[layer] = attrs
  
      # Add ParallelizationFactor to Conv1D/2D
      pf_layers = [
          Conv1D,
          Conv2D,
      ]
  
      for layer in pf_layers:
          attrs = self.attribute_map.get(layer, [])
          attrs.append(
              ConfigurableAttribute("parallelization_factor", default=1)
          )
          self.attribute_map[layer] = attrs
  
      # Add ConvImplementation to Convolution+Pooling layers
      cnn_layers = [
          Conv1D,
          Conv2D,
          SeparableConv1D,
          SeparableConv2D,
          DepthwiseConv2D,
          Pooling1D,
          Pooling2D,
      ]
  
      for layer in cnn_layers:
          attrs = self.attribute_map.get(layer, [])
          # attrs.append(ConfigurableAttribute('conv_implementation', value_type=str, default='LineBuffer'))
          attrs.append(
              ChoiceAttribute(
                  "conv_implementation",
                  choices=["LineBuffer", "Encoded"],
                  default="LineBuffer",
              )
          )
          self.attribute_map[layer] = attrs
  
      sep_conv_layers = [SeparableConv1D, SeparableConv2D]
      for layer in sep_conv_layers:
          attrs = self.attribute_map.get(layer, [])
          attrs.append(
              TypeAttribute(
                  "dw_output", default=FixedPrecisionType(18, 8)
              )
          )
          self.attribute_map[layer] = attrs
  
imports: import os
  import sys
  import numpy
  from hls4ml.backends import FPGABackend
  from hls4ml.backends.fpga.fpga_types import APTypeConverter, HLSTypeConverter, VivadoArrayVariableConverter
  from hls4ml.model.attributes import ChoiceAttribute, ConfigurableAttribute, TypeAttribute
  from hls4ml.model.flow import register_flow
  from hls4ml.model.layers import GRU, LSTM, Conv1D, Conv2D, Dense, DepthwiseConv2D, Embedding, GarNet, GarNetStack, GlobalPooling1D, GlobalPooling2D, Layer, Pooling1D, Pooling2D, SeparableConv1D, SeparableConv2D, SimpleRNN, Softmax
  from hls4ml.model.optimizer import get_backend_passes, layer_optimizer
  from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType, PackedType
  from hls4ml.report import parse_vivado_report
  from hls4ml.utils.fixed_point_utils import ceil_log2
label_desc: Use image node_id 2 for calling the VivadoBackend obj's underlying member method code with example usage: obj._register_layer_attributes() without return types