message: Skip only the base class in trt_mode_test.py
diff: @@ -18,7 +18,7 @@ from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  -from unittest import skip # pylint: disable=g-importing-member
  +from unittest import SkipTest # pylint: disable=g-importing-member
  from tensorflow.python.compiler.tensorrt.test import tf_trt_integration_test_base as trt_test
  from tensorflow.python.framework import dtypes
  @@ -27,7 +27,6 @@ from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
  -@skip("TrtModeTestBase defines a common base class for other tests")
  class TrtModeTestBase(trt_test.TfTrtIntegrationTestBase):
  """Test squeeze on batch dim and some unary operations in TF-TRT."""
  @@ -66,6 +65,12 @@ class TrtModeTestBase(trt_test.TfTrtIntegrationTestBase):
  use_implicit_batch=implicit_batch)
  return conversion_params._replace(rewriter_config_template=rewriter_config)
  + @classmethod
  + def setUpClass(cls):
  + if cls is TrtModeTestBase:
  + raise SkipTest("TrtModeTestBase defines base class for other test.")
  + super(TrtModeTestBase, cls).setUpClass()
  +
  class ImplicitBatchTest(TrtModeTestBase):
  @@ -121,10 +126,7 @@ class DynamicShapesTest(TrtModeTestBase):
  """ Test with dynamic input shape.
  The difference compered to ExplicitBatchTest is that we specify an input and
  - output mask with unknown shapes. Since shape optimization profiles are not
  - yet used in TRTEngineOp, the engine creation will fail and the inference is
  - done using the native segment. The trt_engine_op_test (C++) is more relevant
  - to test the dynamic shapes in its current status.
  + output mask with unknown shapes.
  """
  def GetParams(self):
  