code: def setUp(self):
      if self.dtype == numpy.float16:
          eps = 1e-2
          self.check_forward_options = {"rtol": 5e-3, "atol": 5e-3}
          self.check_backward_options = {
              "eps": eps,
              "rtol": 5e-2,
              "atol": 5e-2,
          }
          self.check_double_backward_options = {
              "eps": eps,
              "rtol": 5e-2,
              "atol": 5e-2,
          }
      elif self.dtype == numpy.float32:
          eps = 1e-3
          self.check_forward_options = {"rtol": 1e-4, "atol": 1e-4}
          self.check_backward_options = {
              "eps": eps,
              "rtol": 5e-4,
              "atol": 5e-4,
          }
          self.check_double_backward_options = {
              "eps": eps,
              "rtol": 1e-3,
              "atol": 1e-3,
          }
      elif self.dtype == numpy.float64:
          eps = 1e-3
          self.check_forward_options = {"rtol": 1e-4, "atol": 1e-4}
          self.check_backward_options = {
              "eps": eps,
              "rtol": 5e-4,
              "atol": 5e-4,
          }
          self.check_double_backward_options = {
              "eps": eps,
              "rtol": 1e-3,
              "atol": 1e-3,
          }
      else:
          assert False
  
      # Sample differentiable inputs
      x_shape = (self.batchsize, self.input_dim)
  
      while True:
          self.a = numpy.random.uniform(-1, 1, x_shape).astype(
              self.dtype
          )
          self.p = numpy.random.uniform(-1, 1, x_shape).astype(
              self.dtype
          )
          self.n = numpy.random.uniform(-1, 1, x_shape).astype(
              self.dtype
          )
          if (abs(self.a - self.p) < 2 * eps).any():
              continue
          if (abs(self.a - self.n) < 2 * eps).any():
              continue
          dist = (
              numpy.sum(
                  (self.a - self.p) ** 2 - (self.a - self.n) ** 2,
                  axis=1,
              )
              + self.margin
          )
          # TODO(imanishi): Investigate whether this condition is enough
          # to dodge non-differentialble points.
          if (abs(dist) < 4 * eps).any():
              continue
          break
  
      if self.reduce == "mean":
          gy_shape = ()
      else:
          gy_shape = (self.batchsize,)
      self.gy = numpy.random.uniform(-1, 1, gy_shape).astype(self.dtype)
      self.gga = numpy.random.uniform(-1, 1, x_shape).astype(self.dtype)
      self.ggp = numpy.random.uniform(-1, 1, x_shape).astype(self.dtype)
      self.ggn = numpy.random.uniform(-1, 1, x_shape).astype(self.dtype)
  
imports: import unittest
  import numpy
  import six
  import chainer
  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 TestTriplet obj's underlying member method code with example usage: obj.setUp() without return types