code: def setUp(self):
      self.batches = 2
      self.in_channels_a_group = 3
      self.out_channels_a_group = 2
      self.in_channels = self.in_channels_a_group * self.groups
      self.out_channels = self.out_channels_a_group * self.groups
      self.kh, self.kw = (3, 3)
      self.stride = 2
      self.pad = (
          int(self.kh / 2) * self.dilate,
          int(self.kw / 2) * self.dilate,
      )
  
      self.check_forward_options.update({"atol": 5e-4, "rtol": 5e-3})
      self.check_backward_options.update({"atol": 5e-4, "rtol": 5e-3})
      self.check_double_backward_options.update(
          {"atol": 5e-4, "rtol": 5e-3}
      )
      self.old_numpy_fp16 = False
      if numpy.float16 in (self.x_dtype, self.W_dtype):
          # Old numpy versions have a bug in the fp16 conversion
          # that happens on the matrix multiplication for the grouped
          # convolution, outputs will be zeroed but computations
          # will be performed in order to detect other issues
          old_numpy = (
              numpy.lib.NumpyVersion(numpy.__version__) < "1.17.0"
          )
          self.old_numpy_fp16 = (
              old_numpy
              and self.groups == 2
              and self.x_dtype == self.W_dtype
          )
          self.check_forward_options.update(
              {"atol": 1e-3, "rtol": 1e-2}
          )
          self.check_backward_options.update(
              {"atol": 1e-3, "rtol": 1e-3}
          )
          self.check_double_backward_options.update(
              {"atol": 1e-2, "rtol": 1e-2}
          )
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import backend
label_desc: Use image node_id 1 for calling the TestConvolution2DFunction obj's underlying member method code with example usage: obj.setUp() without return types