code: def get_expected_hidden_size(
      self, input: Tensor, batch_sizes: Optional[Tensor]
  ) -> Tuple[int, int, int]:
      if batch_sizes is not None:
          mini_batch = int(batch_sizes[0])
      else:
          mini_batch = (
              input.size(0) if self.batch_first else input.size(1)
          )
      num_directions = 2 if self.bidirectional else 1
      expected_hidden_size = (
          self.num_layers * num_directions,
          mini_batch,
          self.hidden_size,
      )
      return expected_hidden_size
  
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 QuantizedRNNBase obj's underlying member method code with example usage: obj.get_expected_hidden_size(input, batch_sizes) and returns: expected_hidden_size