code: def setUp(self):
      compute_capability = int(device.get_compute_capability())
      if compute_capability < 70 and "e" in self.dtype_combo:
          pytest.skip("Not supported")
      dtype_chars = list(self.dtype_combo)
      self.a_dtype = numpy.dtype(dtype_chars[0])
      self.b_dtype = numpy.dtype(dtype_chars[1])
      self.c_dtype = numpy.dtype(dtype_chars[2])
      self.tol = self._tol[dtype_chars[2].lower()]
      self.compute_type = _linalg.COMPUTE_TYPE_DEFAULT
      if self.compute_type_hint == "down-convert":
          if self.c_dtype.char in "fF":
              self.compute_type = _linalg.COMPUTE_TYPE_FP16
              self.tol = self._tol["e"]
          elif self.c_dtype.char in "dD":
              self.compute_type = _linalg.COMPUTE_TYPE_FP32
              self.tol = self._tol["f"]
      elif self.compute_type_hint == "TF32":
          if self.c_dtype.char in "fF":
              self.compute_type = _linalg.COMPUTE_TYPE_TF32
              self.tol = self._tol["e"]
      m, n, k = self.shape
      self.a = self.make_matrix((m, k), self.a_dtype)
      self.b = self.make_matrix((k, n), self.b_dtype)
      self.c = self.make_matrix((m, n), self.c_dtype)
      self.c_ref = self.alpha * cupy.matmul(self.a, self.b)
      self.c_ref += self.beta * self.c
      old_compute_type = cupy._core.get_compute_type(self.c_dtype)
      cupy._core.set_compute_type(self.c_dtype, self.compute_type)
      yield
      cupy._core.set_compute_type(self.c_dtype, old_compute_type)
  
imports: import gc
  import numpy
  import pytest
  import cupy
  from cupy._core import _routines_linalg
  from cupy import testing
  from cupy.cuda import device
  from cupy.cuda import cutensor
label_desc: Use image node_id 3 for calling the TestCuTensorContraction obj's underlying member method code with example usage: obj.setUp() without return types