code: def forward_packed(
      self, input: PackedSequence, hx: Optional[Tensor] = None
  ) -> Tuple[PackedSequence, Tensor]:
      input_, batch_sizes, sorted_indices, unsorted_indices = input
      max_batch_size = int(batch_sizes[0])
  
      output, hidden = self.forward_impl(
          input_, hx, batch_sizes, max_batch_size, sorted_indices
      )
  
      output = PackedSequence(
          output, batch_sizes, sorted_indices, unsorted_indices
      )
      return output, self.permute_hidden(hidden, unsorted_indices)
  
imports: import warnings
  from typing import List, Optional, Tuple
  import torch
  from torch import _VF, Tensor
  from torch.nn.utils.rnn import PackedSequence
label_desc: Use image node_id 4 for calling the QuantizedGRU obj's underlying member method code with example usage: obj.forward_packed(input, hx) and returns: output,  self