code: def check_forward_args(self, input, hidden, batch_sizes):
      is_input_packed = batch_sizes is not None
      expected_input_dim = 2 if is_input_packed else 3
      if input.dim() != expected_input_dim:
          raise RuntimeError(
              "input must have {} dimensions, got {}".format(
                  expected_input_dim, input.dim()
              )
          )
      if self.input_size != input.size(-1):
          raise RuntimeError(
              "input.size(-1) must be equal to input_size. Expected {}, got {}".format(
                  self.input_size, input.size(-1)
              )
          )
  
      if is_input_packed:
          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,
      )
  
      def check_hidden_size(
          hx,
          expected_hidden_size,
          msg="Expected hidden size {}, got {}",
      ):
          if tuple(hx.size()) != expected_hidden_size:
              raise RuntimeError(
                  msg.format(expected_hidden_size, tuple(hx.size()))
              )
  
      if self.mode == "LSTM":
          check_hidden_size(
              hidden[0],
              expected_hidden_size,
              "Expected hidden[0] size {}, got {}",
          )
          check_hidden_size(
              hidden[1],
              expected_hidden_size,
              "Expected hidden[1] size {}, got {}",
          )
      else:
          check_hidden_size(hidden, expected_hidden_size)
  
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 5 for calling the QuantRNNBase obj's underlying member method code with example usage: obj.check_forward_args(input, hidden, batch_sizes) without return types