code: def __setstate__(self, d):
      super(QuantRNNBase, self).__setstate__(d)
      self.__dict__.setdefault("_data_ptrs", [])
      if "all_weights" in d:
          self._all_weights = d["all_weights"]
      if isinstance(self._all_weights[0][0], str):
          return
      num_layers = self.num_layers
      num_directions = 2 if self.bidirectional else 1
      self._all_weights = []
      for layer in range(num_layers):
          for direction in range(num_directions):
              suffix = "_reverse" if direction == 1 else ""
              weights = [
                  "weight_ih_l{}{}",
                  "weight_hh_l{}{}",
                  "bias_ih_l{}{}",
                  "bias_hh_l{}{}",
              ]
              weights = [x.format(layer, suffix) for x in weights]
              if self.bias:
                  self._all_weights += [weights]
              else:
                  self._all_weights += [weights[:2]]
  
imports: import math
  import torch
  import warnings
  import itertools
  import numbers
  from torch import nn
  from torch.nn import Parameter
  from torch.nn.utils.rnn import PackedSequence
  from csrc.pytorch.pytorch_quantization import tensor_quant
  from csrc.pytorch.pytorch_quantization.nn._functions import quant_rnn
  from .None import _utils
label_desc: Use image node_id 8 for calling the QuantRNNBase obj's underlying member method code with example usage: obj.__setstate__(d) without return types