code: def __init__(self, input_size, hidden_size, bias=True, gru="AGRU"):
      super(DynamicRNN, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
  
      if gru == "AGRU":
          self.rnn = AGRUCell(input_size, hidden_size, bias)
      elif gru == "AUGRU":
          self.rnn = AUGRUCell(input_size, hidden_size, bias)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import xavier_normal_, constant_
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence
  from recbole.utils import ModelType, InputType, FeatureType
  from recbole.model.layers import FMEmbedding, MLPLayers, ContextSeqEmbLayer, SequenceAttLayer
  from recbole.model.abstract_recommender import SequentialRecommender
label_desc: Use image node_id 1 to create a new DynamicRNN object from inherited base classes: nn with example: obj = DynamicRNN(input_size, hidden_size, bias, gru)