code: def setUp(self):
      self.x = numpy.random.uniform(-1, 1, (3, 2, 3)).astype(
          numpy.float32
      )
      self.W = numpy.random.uniform(-1, 1, (2)).astype(numpy.float32)
      self.b = numpy.random.uniform(-1, 1, (2)).astype(numpy.float32)
      self.y_expected = numpy.copy(self.x)
      for i, j, k in numpy.ndindex(self.y_expected.shape):
          self.y_expected[i, j, k] *= self.W[j]
          if self.bias_term:
              self.y_expected[i, j, k] += self.b[j]
      self.gy = numpy.random.uniform(-1, 1, (3, 2, 3)).astype(
          numpy.float32
      )
  
      bias_term = self.bias_term
      bias_shape = self.bias_shape
      axis = 1
      if self.learn_W:
          self.link = links.Scale(
              axis, self.W.shape, bias_term, bias_shape
          )
          self.link.W.data = self.W
          if bias_term:
              self.link.bias.b.data = self.b
      else:
          self.link = links.Scale(axis, None, bias_term, bias_shape)
          if bias_term:
              self.link.bias.b.data = self.b
      self.link.cleargrads()
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import gradient_check
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
label_desc: Use image node_id 1 for calling the TestScale obj's underlying member method code with example usage: obj.setUp() without return types