code: def forward(self, inputs):
      xp = backend.get_array_module(*inputs)
      c_prev1, c_prev2, x1, x2, c_next, gc, gh = inputs
  
      gx1 = xp.empty_like(x1)
      gx2 = xp.empty_like(x2)
      ga1, gi1, gf1, go1 = _extract_gates(gx1)
      ga2, gi2, gf2, go2 = _extract_gates(gx2)
  
      if gc is None:
          gc = 0
      if gh is None:
          gh = 0
  
      a1, i1, f1, o1 = _extract_gates(x1)
      a2, i2, f2, o2 = _extract_gates(x2)
      if xp is numpy:
          if intel64.should_use_ideep(">=auto"):
              xp = intel64.ideep.get_array_module(x1)
          tanh_a1 = xp.tanh(a1)
          sig_i1 = _sigmoid(i1, xp)
          sig_f1 = _sigmoid(f1, xp)
          tanh_a2 = xp.tanh(a2)
          sig_i2 = _sigmoid(i2, xp)
          sig_f2 = _sigmoid(f2, xp)
          sig_o = _sigmoid(o1 + o2, xp)
  
          co = xp.tanh(c_next)
          # multiply f later
          gc_prev = gh * sig_o * _grad_tanh(co) + gc
          ga1[:] = gc_prev * sig_i1 * _grad_tanh(tanh_a1)
          gi1[:] = gc_prev * tanh_a1 * _grad_sigmoid(sig_i1)
          gf1[:] = gc_prev * c_prev1 * _grad_sigmoid(sig_f1)
          go1[:] = gh * co * _grad_sigmoid(sig_o)
          ga2[:] = gc_prev * sig_i2 * _grad_tanh(tanh_a2)
          gi2[:] = gc_prev * tanh_a2 * _grad_sigmoid(sig_i2)
          gf2[:] = gc_prev * c_prev2 * _grad_sigmoid(sig_f2)
          go2[:] = gh * co * _grad_sigmoid(sig_o)
          # multiply f here
          gc_prev1 = gc_prev * sig_f1
          gc_prev2 = gc_prev * sig_f2
      else:
          a1, i1, f1, o1 = _extract_gates(x1)
          a2, i2, f2, o2 = _extract_gates(x2)
          gc_prev1 = xp.empty_like(c_prev1)
          gc_prev2 = xp.empty_like(c_prev2)
          cuda.elementwise(
              """T c_prev1, T a1, T i1, T f1, T o1,
                  T c_prev2, T a2, T i2, T f2, T o2,
                  T c, T gc, T gh""",
              """T gc_prev1, T ga1, T gi1, T gf1, T go1,
                  T gc_prev2, T ga2, T gi2, T gf2, T go2""",
              """
                      COMMON_ROUTINE;
                      T co = tanh(c);
                      T temp = gh * ao * grad_tanh(co) + gc;
                      ga1 = temp * ai1 * grad_tanh(aa1);
                      gi1 = temp * aa1 * grad_sigmoid(ai1);
                      gf1 = temp * c_prev1 * grad_sigmoid(af1);
                      go1 = gh * co * grad_sigmoid(ao);
                      gc_prev1 = temp * af1;
                      ga2 = temp * ai2 * grad_tanh(aa2);
                      gi2 = temp * aa2 * grad_sigmoid(ai2);
                      gf2 = temp * c_prev2 * grad_sigmoid(af2);
                      go2 = gh * co * grad_sigmoid(ao);
                      gc_prev2 = temp * af2;
                  """,
              "lstm_bwd",
              preamble=_preamble,
          )(
              c_prev1,
              a1,
              i1,
              f1,
              o1,
              c_prev2,
              a2,
              i2,
              f2,
              o2,
              c_next,
              gc,
              gh,
              gc_prev1,
              ga1,
              gi1,
              gf1,
              go1,
              gc_prev2,
              ga2,
              gi2,
              gf2,
              go2,
          )
  
      return gc_prev1, gc_prev2, gx1, gx2
  
imports: import numpy
  import six
  from chainer import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import function
  from chainer import function_node
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 1 for calling the SLSTMGrad obj's underlying member method code with example usage: obj.forward(inputs) and returns: gc_prev1,  gc_prev2,  gx1,  gx2