code: def setUp(self):
      self.x = numpy.random.uniform(size=self.in_shape).astype(
          numpy.float32
      )
      p1 = [[-0.5], [-0.5]]
      p2 = [[3.5], [3.5]]
      p3 = [[2], [3.5]]
      p4 = [[-0.5], [2]]
      self.grid = numpy.concatenate((p1, p2, p3, p4), axis=1)
      self.grid = self.grid.reshape(1, 2, 4, 1).astype(numpy.float32)
      # Scale the coordinates so that the pixels inside the input image
      # lies in range [-1, 1].
      self.grid[:, 0] = (
          (self.grid[:, 0] / (self.in_shape[3] - 1)) - 0.5
      ) * 2
      self.grid[:, 1] = (
          (self.grid[:, 1] / (self.in_shape[2] - 1)) - 0.5
      ) * 2
  
      exp_p1 = self.x[0, :, 0, 0] / 4
      exp_p2 = self.x[0, :, 3, 3] / 4
      exp_p3 = self.x[0, :, 3, 2] / 2
      exp_p4 = self.x[0, :, 2, 0] / 2
  
      self.expected = numpy.concatenate(
          (
              exp_p1[:, None],
              exp_p2[:, None],
              exp_p3[:, None],
              exp_p4[:, None],
          ),
          axis=1,
      )
      self.expected = self.expected.reshape(1, 2, 4, 1).astype(
          numpy.float32
      )
  
imports: import unittest
  import numpy
  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
  from chainer.testing import condition
  from chainer import Variable
label_desc: Use image node_id 1 for calling the TestSpatialTransformerSamplerForwardPaddedImage obj's underlying member method code with example usage: obj.setUp() without return types