code: def forward(self, input, c0=None):
      assert input.dim() == 2 or input.dim() == 3
      n_in, n_out = self.n_in, self.n_out
      batch = input.size(-2)
      if c0 is None:
          c0 = input.data.new(
              batch, n_out if not self.bidirectional else n_out * 2
          ).zero_()
  
      if self.training and (self.rnn_dropout > 0):
          mask = self.get_dropout_mask_((batch, n_in), self.rnn_dropout)
          x = input * mask.expand_as(input)
      else:
          x = input
  
      x_2d = x if x.dim() == 2 else x.contiguous().view(-1, n_in)
      u = x_2d.mm(self.weight)
  
      if self.training and (self.dropout > 0):
          bidir = 2 if self.bidirectional else 1
          mask_h = self.get_dropout_mask_(
              (batch, n_out * bidir), self.dropout
          )
          h, c = SRU_Compute(
              self.activation_type, n_out, self.bidirectional
          )(u, input, self.bias, c0, mask_h)
      else:
          h, c = SRU_Compute(
              self.activation_type, n_out, self.bidirectional
          )(u, input, self.bias, c0)
  
      return h, c
  
imports: import subprocess
  import platform
  import os
  import re
  import configargparse
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.cuda.amp import custom_fwd, custom_bwd
  from collections import namedtuple
label_desc: Use image node_id 4 for calling the SRUCell obj's underlying member method code with example usage: obj.forward(input, c0) and returns: h,  c