code: def setUp(self):
      self.link = links.SimplifiedDropconnect(
          self.in_size_or_none, self.out_size
      )
      temp_x = numpy.random.uniform(-1, 1, (4, self.in_size)).astype(
          numpy.float32
      )
      self.link(chainer.Variable(temp_x))
      W = self.link.W.data
      W[...] = numpy.random.uniform(-1, 1, W.shape)
      b = self.link.b.data
      b[...] = numpy.random.uniform(-1, 1, b.shape)
      self.link.cleargrads()
      mask_shape = (4, self.out_size, self.in_size)
      self.mask = gen_mask(self.ratio, mask_shape)
  
      x_shape = (4,) + self.in_shape
      self.x = numpy.random.uniform(-1, 1, x_shape).astype(
          numpy.float32
      )
      self.gy = numpy.random.uniform(-1, 1, (4, self.out_size)).astype(
          numpy.float32
      )
      W = (W * self.mask) * (1.0 / (1 - self.ratio))
  
      # numpy 1.9 does not support matmul.
      # So we use numpy.einsum instead of numpy.matmul.
      self.y_expect = (
          numpy.einsum("ijk,ikl->ijl", W, self.x[:, :, None]).reshape(
              4, -1
          )
          + b
      )
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import gradient_check
  from chainer import links
  from chainer.serializers import npz
  from chainer import testing
  from chainer.testing import attr
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling the TestSimplifiedDropconnectParameterShapePlaceholder obj's underlying member method code with example usage: obj.setUp() without return types