code: def setUp(self):
      c_prev1 = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(
          self.dtype
      )
      c_prev2 = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(
          self.dtype
      )
      x1 = numpy.random.uniform(-1, 1, (3, 8, 4)).astype(self.dtype)
      x2 = numpy.random.uniform(-1, 1, (3, 8, 4)).astype(self.dtype)
      c_next = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(self.dtype)
  
      gc = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(self.dtype)
      gh = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(self.dtype)
  
      ggc_prev1 = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(
          self.dtype
      )
      ggc_prev2 = numpy.random.uniform(-1, 1, (3, 2, 4)).astype(
          self.dtype
      )
      ggx1 = numpy.random.uniform(-1, 1, (3, 8, 4)).astype(self.dtype)
      ggx2 = numpy.random.uniform(-1, 1, (3, 8, 4)).astype(self.dtype)
  
      self.inputs = [c_prev1, c_prev2, x1, x2, c_next, gc, gh]
      self.grad_outputs = [ggc_prev1, ggc_prev2, ggx1, ggx2]
  
      self.check_backward_options = {"dtype": numpy.float64}
      if self.dtype == numpy.float16:
          self.check_backward_options = {
              "dtype": numpy.float64,
              "atol": 1e-3,
              "rtol": 1e-2,
          }
  
imports: import unittest
  import numpy
  import six
  from chainer.backends import cuda
  from chainer import functions
  from chainer.functions.rnn import slstm
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import backend
label_desc: Use image node_id 1 for calling the TestSLSTMGrad obj's underlying member method code with example usage: obj.setUp() without return types