old_contents: """
  A collection of popular datasets for deep learning
  ==================================================
  
  dbcollection is a library for downloading/parsing/managing datasets via simple methods.
  It was built from the ground up to be cross-platform (Windows, Linux, MacOS) and
  cross-language (Python, Lua, Matlab, etc.). This is achieved by using the popular HDF5
  file format to store (meta)data of manually parsed datasets and the power of Python for
  scripting. By doing so, this library can target any platform that supports Python and
  any language that has bindings for HDF5.
  
  This package allows to easily manage and load datasets by using HDF5 files to store
  metadata. By storing all the necessary metadata to disk, managing either big or small
  datasets equally impacts the system's resources usage in the same way.
  Also, once a dataset is setup, it is setup forever! This means users can reuse any
  previously set dataset as many times as needed without having to set it each time they
  are used. This allows users to focus on more important tasks and speed up prototyping
  without having to incurr in spending time managing datasets or creating/modyfing
  scripts to load/fetch data from disk.
  
  Main features
  -------------
  
  Here are some of key features dbcollection provides:
  
  - Simple API to load/download/setup/manage datasets.
  - Simple API to fetch data from a dataset.
  - Store and pull data from disk or from memory, you choose!
  - Datasets only need to be set/processed once, so next time you use it it will load instantly!
  - Cross-platform (Windows, Linux, MacOs).
  - Cross-language (Python, Lua/Torch7, Matlab).
  - Easily extensible to other languages that support HDF5 files format.
  - Concurrent/parallel data access thanks to HDF5.
  - Contains a diverse (and growing!) list of popular datasets for machine-, deep-learning tasks
    (object detection, action recognition, human pose estimation, etc.)
  
  """
  
  
  import pkg_resources
  
  # API methods
  from dbcollection.core.api.download import download
  from dbcollection.core.api.process import process
  from dbcollection.core.api.load import load
  from dbcollection.core.api.add import add
  from dbcollection.core.api.remove import remove
  from dbcollection.core.api.config_cache import config_cache
  from dbcollection.core.api.query import query
  from dbcollection.core.api.info import info_cache, info_datasets
  from dbcollection.core.api.list_datasets import fetch_list_datasets
  
  # load the cache file
  from dbcollection.core.cache import CacheManager
  cache = CacheManager()
  
  # package version
  __version__ = pkg_resources.get_distribution('dbcollection').version
  
  # load information about the available datasets for download
  available_datasets_list = fetch_list_datasets()
  
new_contents: """
  A collection of popular datasets for deep learning
  ==================================================
  
  **dbcollection** is a library for downloading/parsing/managing datasets via simple methods.
  It was built from the ground up to be cross-platform (**Windows**, **Linux**, **MacOS**) and
  cross-language (**Python**, **Lua**, **Matlab**, etc.). This is achieved by using the popular ``HDF5``
  file format to store (meta)data of manually parsed datasets and the power of Python for
  scripting. By doing so, this library can target any platform that supports Python and
  any language that has bindings for ``HDF5``.
  
  This package allows to easily manage and load datasets by using ``HDF5`` files to store
  metadata. By storing all the necessary metadata to disk, managing either big or small
  datasets has an equal or very similar impact on the system's resource usage.
  Also, once a dataset is setup, it is setup forever! This means users can reuse any
  previously set dataset as many times as needed without having to set it each time they
  are used.
  
  **dbcollection** allows users to focus on more important tasks like prototyping new models
  or testing them in different datasets without having to incur the loss of spending time managing
  datasets or creating/modyfing scripts to load/fetch data by taking advantage
  of the work of the community that shared these resources.
  
  Main features
  -------------
  
  Here are some of key features dbcollection provides:
  
  - Simple API to load/download/setup/manage datasets.
  - Simple API to fetch data from a dataset.
  - Store and pull data from disk or from memory, you choose!
  - Datasets only need to be set/processed once, so next time you use it it will load instantly!
  - Cross-platform (**Windows**, **Linux**, **MacOs**).
  - Cross-language (**Python**, **Lua/Torch7**, **Matlab**).
  - Easily extensible to other languages that support ``HDF5`` files format.
  - Concurrent/parallel data access thanks to ``HDF5``.
  - Contains a diverse (and growing!) list of popular datasets for machine-, deep-learning tasks
    (*object detection*, *action recognition*, *human pose estimation*, etc.)
  
  """
  
  
  import pkg_resources
  
  # API methods
  from dbcollection.core.api.download import download
  from dbcollection.core.api.process import process
  from dbcollection.core.api.load import load
  from dbcollection.core.api.add import add
  from dbcollection.core.api.remove import remove
  from dbcollection.core.api.config_cache import config_cache
  from dbcollection.core.api.query import query
  from dbcollection.core.api.info import info_cache, info_datasets
  from dbcollection.core.api.list_datasets import fetch_list_datasets
  
  # load the cache file
  from dbcollection.core.cache import CacheManager
  cache = CacheManager()
  
  # package version
  __version__ = pkg_resources.get_distribution('dbcollection').version
  
  # load information about the available datasets for download
  available_datasets_list = fetch_list_datasets()
  
subject: Update main module docstring