code: def __init__(self, config, dataset):
      super().__init__()
  
      self.n_layers = config["n_layers"]
      self.n_heads = config["n_heads"]
      self.hidden_size = config["embedding_size"]
      self.inner_size = config["inner_size"]
      self.hidden_dropout_prob = config["hidden_dropout_prob"]
      self.attn_dropout_prob = config["attn_dropout_prob"]
      self.hidden_act = config["hidden_act"]
      self.layer_norm_eps = config["layer_norm_eps"]
      self.initializer_range = config["initializer_range"]
  
      self.position_embedding = nn.Embedding(
          dataset.field2seqlen[
              config["ITEM_ID_FIELD"] + config["LIST_SUFFIX"]
          ],
          self.hidden_size,
      )
      self.trm_encoder = TransformerEncoder(
          n_layers=self.n_layers,
          n_heads=self.n_heads,
          hidden_size=self.hidden_size,
          inner_size=self.inner_size,
          hidden_dropout_prob=self.hidden_dropout_prob,
          attn_dropout_prob=self.attn_dropout_prob,
          hidden_act=self.hidden_act,
          layer_norm_eps=self.layer_norm_eps,
      )
  
      self.LayerNorm = nn.LayerNorm(
          self.hidden_size, eps=self.layer_norm_eps
      )
      self.dropout = nn.Dropout(self.hidden_dropout_prob)
      self.fn = nn.Linear(self.hidden_size, 1)
  
      self.apply(self._init_weights)
  
imports: import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
label_desc: Use image node_id 1 to create a new TransNet object from inherited base classes: nn with example: obj = TransNet(config, dataset)