code: def _reset_weights_to_compatibility_mode(self):
      np.random.seed(42)
      if self.constrained_embedding:
          n_input = self.layers[-1]
      elif self.embedding:
          n_input = self.embedding
          self.E.weight.set_(
              torch.tensor(
                  self._init_numpy_weights((self.n_items, n_input)),
                  device=self.E.weight.device,
              )
          )
      else:
          n_input = self.n_items
          m = []
          m.append(self._init_numpy_weights((n_input, self.layers[0])))
          m.append(self._init_numpy_weights((n_input, self.layers[0])))
          m.append(self._init_numpy_weights((n_input, self.layers[0])))
          self.GE.Wx0.weight.set_(
              torch.tensor(
                  np.hstack(m), device=self.GE.Wx0.weight.device
              )
          )
          m2 = []
          m2.append(
              self._init_numpy_weights((self.layers[0], self.layers[0]))
          )
          m2.append(
              self._init_numpy_weights((self.layers[0], self.layers[0]))
          )
          self.GE.Wrz0.set_(
              torch.tensor(np.hstack(m2), device=self.GE.Wrz0.device)
          )
          self.GE.Wh0.set_(
              torch.tensor(
                  self._init_numpy_weights(
                      (self.layers[0], self.layers[0])
                  ),
                  device=self.GE.Wh0.device,
              )
          )
          self.GE.Bh0.set_(
              torch.zeros(
                  (self.layers[0] * 3,), device=self.GE.Bh0.device
              )
          )
      for i in range(self.start, len(self.layers)):
          m = []
          m.append(self._init_numpy_weights((n_input, self.layers[i])))
          m.append(self._init_numpy_weights((n_input, self.layers[i])))
          m.append(self._init_numpy_weights((n_input, self.layers[i])))
          self.G[i].weight_ih.set_(
              torch.tensor(
                  np.vstack(m), device=self.G[i].weight_ih.device
              )
          )
          m2 = []
          m2.append(
              self._init_numpy_weights((self.layers[i], self.layers[i]))
          )
          m2.append(
              self._init_numpy_weights((self.layers[i], self.layers[i]))
          )
          m2.append(
              self._init_numpy_weights((self.layers[i], self.layers[i]))
          )
          self.G[i].weight_hh.set_(
              torch.tensor(
                  np.vstack(m2), device=self.G[i].weight_hh.device
              )
          )
          self.G[i].bias_hh.set_(
              torch.zeros(
                  (self.layers[i] * 3,), device=self.G[i].bias_hh.device
              )
          )
          self.G[i].bias_ih.set_(
              torch.zeros(
                  (self.layers[i] * 3,), device=self.G[i].bias_ih.device
              )
          )
      self.Wy.weight.set_(
          torch.tensor(
              self._init_numpy_weights((self.n_items, self.layers[-1])),
              device=self.Wy.weight.device,
          )
      )
      self.By.weight.set_(
          torch.zeros((self.n_items, 1), device=self.By.weight.device)
      )
  
imports: from collections import Counter
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Variable
  from torch.optim import Optimizer
  from cornac.utils.common import get_rng
label_desc: Use image node_id 9 for calling the GRU4RecModel obj's underlying member method code with example usage: obj._reset_weights_to_compatibility_mode() without return types