code: def forward(
      self, input: Tensor, hx: Optional[Tensor] = None
  ) -> Tensor:
      self.check_forward_input(input)
      if hx is None:
          hx = torch.zeros(
              input.size(0),
              self.hidden_size,
              dtype=input.dtype,
              device=input.device,
          )
      self.check_forward_hidden(input, hx, "")
      return _VF.quantized_gru_cell(
          input,
          hx,
          self.weight_ih,
          self.weight_hh,
          self.bias_ih,
          self.bias_hh,
          self.packed_ih,
          self.packed_hh,
          self.col_offsets_ih,
          self.col_offsets_hh,
          self.scale_ih,
          self.scale_hh,
          self.zero_point_ih,
          self.zero_point_hh,
      )
  
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 2 for calling the QuantizedGRUCell obj's underlying member method code with example usage: obj.forward(input, hx) and returns: _VF