code: def setUp(self):
      self.W = numpy.random.uniform(-1, 1, (2, 3)).astype(self.W_dtype)
      self.b = numpy.random.uniform(-1, 1, 2).astype(self.x_dtype)
  
      self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(self.x_dtype)
      self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(self.x_dtype)
      self.ggW = numpy.random.uniform(-1, 1, (2, 3)).astype(
          self.W_dtype
      )
      self.ggb = numpy.random.uniform(-1, 1, 2).astype(self.x_dtype)
      self.ggx = numpy.random.uniform(-1, 1, (4, 3)).astype(
          self.x_dtype
      )
      self.y = self.x.dot(self.W.T) + self.b
      self.check_forward_options = {}
      self.check_backward_options = {"atol": 1e-3, "rtol": 1e-3}
      self.check_double_backward_options = {"atol": 1e-2, "rtol": 1e-2}
      if self.x_dtype == numpy.float16:
          self.check_forward_options = {"atol": 1e-3, "rtol": 1e-2}
          self.check_backward_options = {
              "dtype": numpy.float64,
              "atol": 1e-2,
              "rtol": 1e-2,
          }
          self.check_double_backward_options = {
              "dtype": numpy.float64,
              "atol": 5e-2,
              "rtol": 5e-2,
          }
      elif self.W_dtype == numpy.float16:
          self.check_backward_options = {
              "dtype": numpy.float64,
              "atol": 1e-2,
              "rtol": 1e-2,
          }
          self.check_double_backward_options = {
              "dtype": numpy.float64,
              "atol": 1e-1,
              "rtol": 1e-1,
          }
  
imports: import unittest
  import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
label_desc: Use image node_id 1 for calling the TestSimplifiedDropconnect obj's underlying member method code with example usage: obj.setUp() without return types