code: def forward_tensor(
      self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
  ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
      batch_sizes = None
      max_batch_size = (
          input.size(0) if self.batch_first else input.size(1)
      )
      sorted_indices = None
      unsorted_indices = None
  
      output, hidden = self.forward_impl(
          input, hx, batch_sizes, max_batch_size, sorted_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 3 for calling the QuantizedLSTM obj's underlying member method code with example usage: obj.forward_tensor(input, hx) and returns: output,  self