code: def __init__(
      self, hidden_size, seq_len, num_item, device, dropout_prob
  ):
      super(Explore_Recommendation_Decoder, self).__init__()
      self.dropout = nn.Dropout(dropout_prob)
      self.hidden_size = hidden_size
      self.seq_len = seq_len
      self.num_item = num_item
      self.device = device
      self.We = nn.Linear(hidden_size, hidden_size)
      self.Ue = nn.Linear(hidden_size, hidden_size)
      self.tanh = nn.Tanh()
      self.Ve = nn.Linear(hidden_size, 1)
      self.matrix_for_explore = nn.Linear(
          2 * self.hidden_size, self.num_item, bias=False
      )
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.init import xavier_normal_, constant_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.utils import InputType
label_desc: Use image node_id 1 to create a new Explore_Recommendation_Decoder object from inherited base classes: nn with example: obj = Explore_Recommendation_Decoder(hidden_size, seq_len, num_item, device, dropout_prob)