old_contents: from setuptools import setup
  
  setup(
      name='autograd',
      version='1.1.8',
      description='Efficiently computes derivatives of numpy code.',
      author='Dougal Maclaurin and David Duvenaud and Matthew Johnson',
      author_email="maclaurin@physics.harvard.edu, duvenaud@cs.toronto.edu, mattjj@csail.mit.edu",
      packages=['autograd', 'autograd.numpy', 'autograd.scipy', 'autograd.scipy.stats'],
      install_requires=['numpy>=1.10', 'scipy>=0.17', 'future>=0.15.2'],
      keywords=['Automatic differentiation', 'backpropagation', 'gradients',
                'machine learning', 'optimization', 'neural networks',
                'Python', 'Numpy', 'Scipy'],
      url='https://github.com/HIPS/autograd',
      license='MIT',
      classifiers=['Development Status :: 4 - Beta',
                   'License :: OSI Approved :: MIT License',
                   'Programming Language :: Python :: 2.7',
                   'Programming Language :: Python :: 3.5'],
  )
  
new_contents: from setuptools import setup
  
  setup(
      name='autograd',
      version='1.1.9',
      description='Efficiently computes derivatives of numpy code.',
      author='Dougal Maclaurin and David Duvenaud and Matthew Johnson',
      author_email="maclaurin@physics.harvard.edu, duvenaud@cs.toronto.edu, mattjj@csail.mit.edu",
      packages=['autograd', 'autograd.numpy', 'autograd.scipy', 'autograd.scipy.stats'],
      install_requires=['numpy>=1.10', 'scipy>=0.17', 'future>=0.15.2'],
      keywords=['Automatic differentiation', 'backpropagation', 'gradients',
                'machine learning', 'optimization', 'neural networks',
                'Python', 'Numpy', 'Scipy'],
      url='https://github.com/HIPS/autograd',
      license='MIT',
      classifiers=['Development Status :: 4 - Beta',
                   'License :: OSI Approved :: MIT License',
                   'Programming Language :: Python :: 2.7',
                   'Programming Language :: Python :: 3.5'],
  )
  
subject: bump version for pypi