code: def forward(self, input, hx=None):
      is_packed = isinstance(input, PackedSequence)
      if is_packed:
          input, batch_sizes, sorted_indices, unsorted_indices = input
          max_batch_size = batch_sizes[0]
          max_batch_size = int(max_batch_size)
      else:
          batch_sizes = None
          max_batch_size = (
              input.size(0) if self.batch_first else input.size(1)
          )
  
      if hx is None:
          num_directions = 2 if self.bidirectional else 1
          hx = input.new_zeros(
              self.num_layers * num_directions,
              max_batch_size,
              self.hidden_size,
              requires_grad=False,
          )
          if self.mode == "LSTM":
              hx = (hx, hx)
  
      has_flat_weights = (
          list(p.data.data_ptr() for p in self.parameters())
          == self._data_ptrs
      )
      if has_flat_weights:
          first_data = next(self.parameters()).data
          assert first_data.storage().size() == self._param_buf_size
          flat_weight = first_data.new().set_(
              first_data.storage(),
              0,
              torch.Size([self._param_buf_size]),
          )
      else:
          flat_weight = None
  
      self.check_forward_args(input, hx, batch_sizes)
      func = quant_rnn.RNN(
          self.mode,
          self.input_size,
          self.hidden_size,
          num_layers=self.num_layers,
          batch_first=self.batch_first,
          dropout=self.dropout,
          train=self.training,
          bidirectional=self.bidirectional,
          dropout_state=self.dropout_state,
          variable_length=is_packed,
          flat_weight=flat_weight,
      )
      output, hidden = func(
          input,
          self.all_weights,
          hx,
          batch_sizes,
          self._input_quantizers,
          self._weight_quantizers,
      )
      if is_packed:
          output = PackedSequence(output, batch_sizes)
      return output, hidden
  
imports: import math
  import torch
  import warnings
  import itertools
  import numbers
  from torch import nn
  from torch.nn import Parameter
  from torch.nn.utils.rnn import PackedSequence
  from csrc.pytorch.pytorch_quantization import tensor_quant
  from csrc.pytorch.pytorch_quantization.nn._functions import quant_rnn
  from .None import _utils
label_desc: Use image node_id 6 for calling the QuantRNNBase obj's underlying member method code with example usage: obj.forward(input, hx) and returns: output,  hidden