code: def _init_weight(self, shape):
      mat = torch.FloatTensor(
          np.random.normal(0, self.initializer_range, shape)
      )
      return nn.Parameter(mat, requires_grad=True)
  
file_path: RecBole/recbole/model/sequential_recommender/sine.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the SINE obj's underlying member method code with example usage: obj._init_weight(shape) and returns: nn