old_contents: """
  Scikit-Optimize, or `skopt`, is a simple and efficient library to
  minimize (very) expensive and noisy black-box functions. It implements
  several methods for sequential model-based optimization. `skopt` is reusable
  in many contexts and accessible.
  """
  try:
      # This variable is injected in the __builtins__ by the build
      # process. It is used to enable importing subpackages of sklearn when
      # the binaries are not built
      __SKOPT_SETUP__
  except NameError:
      __SKOPT_SETUP__ = False
  
  
  # PEP0440 compatible formatted version, see:
  # https://www.python.org/dev/peps/pep-0440/
  #
  # Generic release markers:
  #   X.Y
  #   X.Y.Z   # For bugfix releases
  #
  # Admissible pre-release markers:
  #   X.YaN   # Alpha release
  #   X.YbN   # Beta release
  #   X.YrcN  # Release Candidate
  #   X.Y     # Final release
  #
  # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
  # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
  #
  __version__ = "0.9rc1"
  
  if __SKOPT_SETUP__:
      import sys
      sys.stderr.write('Partial import of skopt during the build process.\n')
      # We are not importing the rest of scikit-optimize during the build
      # process, as it may not be compiled yet
  else:
      import platform
      import struct
      from . import acquisition
      from . import benchmarks
      from . import callbacks
      from . import learning
      from . import optimizer
  
      from . import space
      from . import sampler
      from .optimizer import dummy_minimize
      from .optimizer import forest_minimize
      from .optimizer import gbrt_minimize
      from .optimizer import gp_minimize
      from .optimizer import Optimizer
      from .searchcv import BayesSearchCV
      from .space import Space
      from .utils import dump
      from .utils import expected_minimum
      from .utils import expected_minimum_random_sampling
      from .utils import load
      __all__ = (
          "acquisition",
          "benchmarks",
          "callbacks",
          "learning",
          "optimizer",
          "plots",
          "sampler",
          "space",
          "gp_minimize",
          "dummy_minimize",
          "forest_minimize",
          "gbrt_minimize",
          "Optimizer",
          "dump",
          "load",
          "expected_minimum",
          "BayesSearchCV",
          "Space"
      )
      IS_PYPY = platform.python_implementation() == 'PyPy'
      _IS_32BIT = 8 * struct.calcsize("P") == 32
  
new_contents: """
  Scikit-Optimize, or `skopt`, is a simple and efficient library to
  minimize (very) expensive and noisy black-box functions. It implements
  several methods for sequential model-based optimization. `skopt` is reusable
  in many contexts and accessible.
  """
  try:
      # This variable is injected in the __builtins__ by the build
      # process. It is used to enable importing subpackages of sklearn when
      # the binaries are not built
      __SKOPT_SETUP__
  except NameError:
      __SKOPT_SETUP__ = False
  
  
  # PEP0440 compatible formatted version, see:
  # https://www.python.org/dev/peps/pep-0440/
  #
  # Generic release markers:
  #   X.Y
  #   X.Y.Z   # For bugfix releases
  #
  # Admissible pre-release markers:
  #   X.YaN   # Alpha release
  #   X.YbN   # Beta release
  #   X.YrcN  # Release Candidate
  #   X.Y     # Final release
  #
  # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
  # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
  #
  __version__ = "0.9.0"
  
  if __SKOPT_SETUP__:
      import sys
      sys.stderr.write('Partial import of skopt during the build process.\n')
      # We are not importing the rest of scikit-optimize during the build
      # process, as it may not be compiled yet
  else:
      import platform
      import struct
      from . import acquisition
      from . import benchmarks
      from . import callbacks
      from . import learning
      from . import optimizer
  
      from . import space
      from . import sampler
      from .optimizer import dummy_minimize
      from .optimizer import forest_minimize
      from .optimizer import gbrt_minimize
      from .optimizer import gp_minimize
      from .optimizer import Optimizer
      from .searchcv import BayesSearchCV
      from .space import Space
      from .utils import dump
      from .utils import expected_minimum
      from .utils import expected_minimum_random_sampling
      from .utils import load
      __all__ = (
          "acquisition",
          "benchmarks",
          "callbacks",
          "learning",
          "optimizer",
          "plots",
          "sampler",
          "space",
          "gp_minimize",
          "dummy_minimize",
          "forest_minimize",
          "gbrt_minimize",
          "Optimizer",
          "dump",
          "load",
          "expected_minimum",
          "BayesSearchCV",
          "Space"
      )
      IS_PYPY = platform.python_implementation() == 'PyPy'
      _IS_32BIT = 8 * struct.calcsize("P") == 32
  
subject: Update __version__ to 0.9.0