old_contents: 
  import os
  
  # issue#7378 may happen with custom opencv. It doesn't hurt to disable opencl
  os.environ['OPENCV_OPENCL_RUNTIME'] = 'disabled'     # https://github.com/opencv/opencv/pull/10155
  try:
      # issue#1924 may happen on old systems
      import cv2  # noqa
      # cv2.setNumThreads(0)
      if int(cv2.__version__.split('.')[0]) == 3:
          cv2.ocl.setUseOpenCL(False)
      # check if cv is built with cuda or openmp
      info = cv2.getBuildInformation().split('\n')
      for line in info:
          splits = line.split()
          if not len(splits):
              continue
          answer = splits[-1].lower()
          if answer in ['yes', 'no']:
              if 'cuda' in line.lower() and answer == 'yes':
                  # issue#1197
                  print("OpenCV is built with CUDA support. "
                        "This may cause slow initialization or sometimes segfault with TensorFlow.")
          if answer == 'openmp':
              print("OpenCV is built with OpenMP support. This usually results in poor performance. For details, see "
                    "https://github.com/tensorpack/benchmarks/blob/master/ImageNet/benchmark-opencv-resize.py")
  except (ImportError, TypeError):
      pass
  
  os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'  # issue#9339
  os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'   # use more warm-up
  
  # Since 1.3, this is not needed
  os.environ['TF_AVGPOOL_USE_CUDNN'] = '1'   # issue#8566
  
  # TF1.5 features
  os.environ['TF_SYNC_ON_FINISH'] = '0'   # will become default
  os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
  os.environ['TF_GPU_THREAD_COUNT'] = '2'
  
  # Available in TF1.6+ & cudnn7. Haven't seen different performance on R50.
  # NOTE we disable it because:
  # this mode may use scaled atomic integer reduction that may cause a numerical
  # overflow for certain input data range.
  os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '0'
  
  # Available since 1.12. issue#15874
  os.environ['TF_ENABLE_WHILE_V2'] = '1'
  os.environ['TF_ENABLE_COND_V2'] = '1'
  
  try:
      import tensorflow as tf  # noqa
      _version = tf.__version__.split('.')
      assert int(_version[0]) >= 1 and int(_version[1]) >= 3, "TF>=1.3 is required!"
      _HAS_TF = True
  except ImportError:
      print("Failed to import tensorflow.")
      _HAS_TF = False
  
  
  # These lines will be programatically read/write by setup.py
  # Don't touch them.
  __version__ = '0.9.0.1'
  __git_version__ = __version__
  
new_contents: 
  import os
  
  # issue#7378 may happen with custom opencv. It doesn't hurt to disable opencl
  os.environ['OPENCV_OPENCL_RUNTIME'] = 'disabled'     # https://github.com/opencv/opencv/pull/10155
  try:
      # issue#1924 may happen on old systems
      import cv2  # noqa
      # cv2.setNumThreads(0)
      if int(cv2.__version__.split('.')[0]) == 3:
          cv2.ocl.setUseOpenCL(False)
      # check if cv is built with cuda or openmp
      info = cv2.getBuildInformation().split('\n')
      for line in info:
          splits = line.split()
          if not len(splits):
              continue
          answer = splits[-1].lower()
          if answer in ['yes', 'no']:
              if 'cuda' in line.lower() and answer == 'yes':
                  # issue#1197
                  print("OpenCV is built with CUDA support. "
                        "This may cause slow initialization or sometimes segfault with TensorFlow.")
          if answer == 'openmp':
              print("OpenCV is built with OpenMP support. This usually results in poor performance. For details, see "
                    "https://github.com/tensorpack/benchmarks/blob/master/ImageNet/benchmark-opencv-resize.py")
  except (ImportError, TypeError):
      pass
  
  os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'  # issue#9339
  os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'   # use more warm-up
  
  # Since 1.3, this is not needed
  os.environ['TF_AVGPOOL_USE_CUDNN'] = '1'   # issue#8566
  
  # TF1.5 features
  os.environ['TF_SYNC_ON_FINISH'] = '0'   # will become default
  os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
  os.environ['TF_GPU_THREAD_COUNT'] = '2'
  
  # Available in TF1.6+ & cudnn7. Haven't seen different performance on R50.
  # NOTE we disable it because:
  # this mode may use scaled atomic integer reduction that may cause a numerical
  # overflow for certain input data range.
  os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '0'
  
  # Available since 1.12. issue#15874
  os.environ['TF_ENABLE_WHILE_V2'] = '1'
  os.environ['TF_ENABLE_COND_V2'] = '1'
  
  try:
      import tensorflow as tf  # noqa
      _version = tf.__version__.split('.')
      assert int(_version[0]) >= 1 and int(_version[1]) >= 3, "TF>=1.3 is required!"
      _HAS_TF = True
  except ImportError:
      print("Failed to import tensorflow.")
      _HAS_TF = False
  
  
  # These lines will be programatically read/write by setup.py
  # Don't touch them.
  __version__ = '0.9.1'
  __git_version__ = __version__
  
subject: bump version