code: def set_target_reuse_factor(self, layer):
      # TODO update target reuse factor for the RNN layers
      targ_cycles = layer.get_attr("target_cycles")
  
      shuffle_cycles = 6  # Number of clock cycles to move data around
      if targ_cycles is not None:
          if "Dense" in layer.class_name:
              kernel_multiplies = layer.get_attr("n_out")
          elif "Conv1D" in layer.class_name:
              kernel_multiplies = layer.get_attr("out_width")
          elif "Conv2D" in layer.class_name:
              kernel_multiplies = layer.get_attr(
                  "out_height"
              ) * layer.get_attr("out_width")
          else:
              print(
                  f"Unable to set target reuse factor for layer: {layer.name} ({layer.class_name})"
              )
              return
  
          if (
              targ_cycles < shuffle_cycles * kernel_multiplies
          ):  # 6 clock min (6 * out_height * out_width)
              print(
                  "Latency can not be achieved with current target {}. Mininum {}.".format(
                      targ_cycles,
                      shuffle_cycles * kernel_multiplies + 1,
                  )
              )
              return
          else:
              rf = (
                  targ_cycles - shuffle_cycles * kernel_multiplies
              )  # subtract data shuffling overhead
  
          layer.set_attr("reuse_factor", float(rf) / kernel_multiplies)
  
imports: import math
  import os
  import re
  from bisect import bisect_left
  from collections.abc import Iterable
  import numpy
  from hls4ml.backends.backend import Backend
  from hls4ml.model.attributes import ChoiceAttribute, ConfigurableAttribute, TypeAttribute
  from hls4ml.model.layers import GRU, LSTM, Activation, BatchNormalization, Conv1D, Conv2D, Dense, Dot, Embedding, GarNet, GarNetStack, GlobalPooling1D, GlobalPooling2D, Pooling1D, Pooling2D, SeparableConv1D, SeparableConv2D, SimpleRNN, Softmax
  from hls4ml.model.optimizer import model_optimizer
  from hls4ml.model.types import ExponentPrecisionType, FixedPrecisionType, IntegerPrecisionType, RoundingMode, SaturationMode, XnorPrecisionType
  from hls4ml.writer import get_writer
label_desc: Use image node_id 11 for calling the FPGABackend obj's underlying member method code with example usage: obj.set_target_reuse_factor(layer) without return types