code: def setup(self):
      if len(self.in_dtypes) == 3:
          x_dtype, w_dtype, b_dtype = self.in_dtypes
      else:
          (x_dtype, w_dtype), b_dtype = self.in_dtypes, None
  
      x_kind = numpy.dtype(x_dtype).kind
      w_kind = numpy.dtype(w_dtype).kind
      b_kind = None if b_dtype is None else numpy.dtype(b_dtype).kind
  
      device = chainerx.get_default_device()
      if device.backend.name == "cuda" and len(self.x_shape) <= 3:
          # TODO(sonots): Support 1 dimensional convolution with CUDA.
          pytest.skip(
              "cuDNN does not support 1 dimensional convolution and throws "
              "DimensionError"
          )
      if device.backend.name == "cuda" and self.cover_all is True:
          pytest.skip(
              "outsize (for cover_all=True) is not supported by CUDA"
          )
  
      # Skip backward/double-backward tests for int dtypes
      if (
          x_kind != "f"
          and w_kind != "f"
          and (b_kind is None or b_kind != "f")
      ):
          self.skip_backward_test = True
          self.skip_double_backward_test = True
  
      if (
          x_dtype == "float16"
          or w_dtype == "float16"
          or b_dtype == "float16"
      ):
          self.check_forward_options.update(
              {"rtol": 5e-2, "atol": 2e-2}
          )
          self.check_backward_options.update(
              {"eps": 2**-3, "rtol": 1e-1, "atol": 1e-2}
          )
      else:
          self.check_forward_options.update({"rtol": 1e-3})
          self.check_backward_options.update(
              {"eps": 1e-2, "rtol": 1e-3, "atol": 1e-4}
          )
      self.check_double_backward_options.update(
          {"rtol": 5e-2, "atol": 5e-3}
      )
  
      # Determine outsize
      cover_all = self.cover_all
      if cover_all is None:
          outsize = None
      else:
          x_shape = self.x_shape
          w_shape = self.w_shape
          stride = self.stride
          pad = self.pad
          in_dims = x_shape[2:]
          kernel_size = w_shape[2:]
          ndim = len(in_dims)
          stride_tup = (
              (stride,) * ndim if isinstance(stride, int) else stride
          )
          pad_tup = (pad,) * ndim if isinstance(pad, int) else pad
          outsize = tuple(
              chainer.utils.conv.get_deconv_outsize(
                  d, k, s, p, cover_all
              )
              for (d, k, s, p) in zip(
                  in_dims, kernel_size, stride_tup, pad_tup
              )
          )
      self.outsize = outsize
  
imports: import unittest
  import chainer
  from chainer import functions
  import numpy
  import pytest
  import chainerx
  from chainerx_tests import array_utils
  from chainerx_tests import dtype_utils
  from chainerx_tests import op_utils
label_desc: Use image node_id 1 for calling the _ConvTransposeTestBase obj's underlying member method code with example usage: obj.setup() without return types