code: def forward(self, inputs, input_lengths):
      # Routine for fetching the last valid output of a dynamic LSTM with varying input lengths and padding
      packed_seqs = nn.utils.rnn.pack_padded_sequence(
          inputs,
          input_lengths.tolist(),
          batch_first=True,
          enforce_sorted=False,
      )  # dynamic rnn sequence padding
      self.lstm.flatten_parameters()
      _, (ht, _) = self.lstm(packed_seqs)
      last_output = ht[-1]
      return last_output
  
imports: import torch
  from torch import nn
  from torch.distributions.multivariate_normal import MultivariateNormal
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the TextSummary obj's underlying member method code with example usage: obj.forward(inputs, input_lengths) and returns: last_output