code: def setUp(self):
      self.xs = [
          rand_vector((self.batch, self.in_size))
          for _ in range(self.length)
      ]
      h_shape = (self.n_layers, self.batch, self.out_size)
      self.cx = rand_vector(h_shape)
      self.hx = rand_vector(h_shape)
      self.ws = []
      self.bs = []
      for i in range(self.n_layers):
          weights = []
          biases = []
          for j in range(8):
              if i == 0 and j < 4:
                  w_in = self.in_size
              else:
                  w_in = self.out_size
  
              weights.append(rand_vector((self.out_size, w_in)))
              biases.append(rand_vector((self.out_size,)))
  
          self.ws.append(weights)
          self.bs.append(biases)
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import backend
  from chainer.testing import condition
label_desc: Use image node_id 1 for calling the TestNStepLSTMDropout obj's underlying member method code with example usage: obj.setUp() without return types