code: def forward(self, input, att_scores=None, hidden_output=None):
      if not isinstance(input, PackedSequence) or not isinstance(
          att_scores, PackedSequence
      ):
          raise NotImplementedError(
              "DynamicRNN only supports packed input and att_scores"
          )
  
      input, batch_sizes, sorted_indices, unsorted_indices = input
      att_scores = att_scores.data
  
      max_batch_size = int(batch_sizes[0])
      if hidden_output is None:
          hidden_output = torch.zeros(
              max_batch_size,
              self.hidden_size,
              dtype=input.dtype,
              device=input.device,
          )
  
      outputs = torch.zeros(
          input.size(0),
          self.hidden_size,
          dtype=input.dtype,
          device=input.device,
      )
  
      begin = 0
      for batch in batch_sizes:
          new_hx = self.rnn(
              input[begin : begin + batch],
              hidden_output[0:batch],
              att_scores[begin : begin + batch],
          )
          outputs[begin : begin + batch] = new_hx
          hidden_output = new_hx
          begin += batch
  
      return PackedSequence(
          outputs, batch_sizes, sorted_indices, unsorted_indices
      )
  
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 2 for calling the DynamicRNN obj's underlying member method code with example usage: obj.forward(input, att_scores, hidden_output) and returns: PackedSequence