code: def truncated_normal_(self, tensor, mean=0, std=0.09):
      with torch.no_grad():
          size = tensor.shape
          tmp = tensor.new_empty(size + (4,)).normal_()
          valid = (tmp < 2) & (tmp > -2)
          ind = valid.max(-1, keepdim=True)[1]
          tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
          tensor.data.mul_(std).add_(mean)
          return tensor
  
imports: import random
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  import math
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.data.interaction import Interaction
label_desc: Use image node_id 4 for calling the FEARec obj's underlying member method code with example usage: obj.truncated_normal_(tensor, mean, std) and returns: tensor