code: def forward(self, u, x, bias, init=None, mask_h=None):
      bidir = 2 if self.bidirectional else 1
      length = x.size(0) if x.dim() == 3 else 1
      batch = x.size(-2)
      d = self.d_out
      k = u.size(-1) // d
      k_ = k // 2 if self.bidirectional else k
      ncols = batch * d * bidir
      thread_per_block = min(512, ncols)
      num_block = (ncols - 1) // thread_per_block + 1
  
      init_ = x.new(ncols).zero_() if init is None else init
      size = (
          (length, batch, d * bidir)
          if x.dim() == 3
          else (batch, d * bidir)
      )
      c = x.new(*size)
      h = x.new(*size)
  
      FUNC = SRU_FWD_FUNC if not self.bidirectional else SRU_BiFWD_FUNC
      FUNC(
          args=[
              u.contiguous().data_ptr(),
              x.contiguous().data_ptr() if k_ == 3 else 0,
              bias.data_ptr(),
              init_.contiguous().data_ptr(),
              mask_h.data_ptr() if mask_h is not None else 0,
              length,
              batch,
              d,
              k_,
              h.data_ptr(),
              c.data_ptr(),
              self.activation_type,
          ],
          block=(thread_per_block, 1, 1),
          grid=(num_block, 1, 1),
          stream=SRU_STREAM,
      )
  
      self.save_for_backward(u, x, bias, init, mask_h)
      self.intermediate = c
      if x.dim() == 2:
          last_hidden = c
      elif self.bidirectional:
          # -> directions x batch x dim
          last_hidden = torch.stack((c[-1, :, :d], c[0, :, d:]))
      else:
          last_hidden = c[-1]
      return h, last_hidden
  
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 3 for calling the SRU_Compute obj's underlying member method code with example usage: obj.forward(u, x, bias, init, mask_h) and returns: h,  last_hidden