code: def init_lstm(self, layer):
      # TODO Allow getting recurrent reuse factor from the config
      reuse_factor = layer.model.config.get_reuse_factor(layer)
      layer.set_attr("recurrent_reuse_factor", reuse_factor)
  
      if layer.model.config.is_resource_strategy(layer):
          n_in, n_out, n_in_recr, n_out_recr = self.get_layer_mult_size(
              layer
          )
          self.set_closest_reuse_factor(layer, n_in, n_out)
          self.set_closest_reuse_factor(
              layer,
              n_in_recr,
              n_out_recr,
              attribute="recurrent_reuse_factor",
          )
          layer.set_attr("strategy", "resource")
      else:
          layer.set_attr("strategy", "latency")
  
      layer.set_attr(
          "index_t",
          NamedType(
              f"layer{layer.index}_index",
              IntegerPrecisionType(width=1, signed=False),
          ),
      )
  
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 23 for calling the VivadoBackend obj's underlying member method code with example usage: obj.init_lstm(layer) without return types