original_docstring: 
      Loads and preprocesses an example, given its path. The example must be a `*.npy` file that contains an array
      that can (and will) be casted to float32. It also returns the data type and the shape before flattening the array,
      to allow serialization and later reconstruction, and a string to serve as example identifier (e.g. the file name).
  
      :param example_path: Path to the `*.npy` file.
      :type example_path: str
      :return: Flattened numpy array of type np.float32 (it must be compatible with a list of float), data type (to allow
          for serialization and later parsing), data shape before flattening (to allow for unflattening), and example ID.
      :rtype: tuple[numpy.ndarray, type, tuple[int], str]
      
docstring: Loads and preprocesses an example, given its path. The example must be a `*.npy` file that contains an array
  that can (and will) be casted to float32. It also returns the data type and the shape before flattening the array,
  to allow serialization and later reconstruction, and a string to serve as example identifier .
docstring_tokens: Loads and preprocesses an example given its path . The example must be a ` * . npy ` file that contains an array that can ( and will ) be casted to float32 . It also returns the data type and the shape before flattening the array to allow serialization and later reconstruction and a string to serve as example identifier .
code: def npy_data_preprocessor(example_path):
      """
      Loads and preprocesses an example, given its path. The example must be a `*.npy` file that contains an array
      that can (and will) be casted to float32. It also returns the data type and the shape before flattening the array,
      to allow serialization and later reconstruction, and a string to serve as example identifier (e.g. the file name).
  
      :param example_path: Path to the `*.npy` file.
      :type example_path: str
      :return: Flattened numpy array of type np.float32 (it must be compatible with a list of float), data type (to allow
          for serialization and later parsing), data shape before flattening (to allow for unflattening), and example ID.
      :rtype: tuple[numpy.ndarray, type, tuple[int], str]
      """
      data = np.load(example_path).astype(np.float32)
      return data.ravel(), data.dtype, data.shape, Path(example_path).stem
code_tokens: def npy_data_preprocessor ( example_path ) : data = np . load ( example_path ) . astype ( np . float32 ) return data . ravel ( ) , data . dtype , data . shape , Path ( example_path ) . stem
short_docstring: Loads and preprocesses an example, given its path.
short_docstring_tokens: Loads and preprocesses an example given its path .
comment: """
      Loads and preprocesses an example, given its path. The example must be a `*.npy` file that contains an array
      that can (and will) be casted to float32. It also returns the data type and the shape before flattening the array,
      to allow serialization and later reconstruction, and a string to serve as example identifier (e.g. the file name).
  
      :param example_path: Path to the `*.npy` file.
      :type example_path: str
      :return: Flattened numpy array of type np.float32 (it must be compatible with a list of float), data type (to allow
          for serialization and later parsing), data shape before flattening (to allow for unflattening), and example ID.
      :rtype: tuple[numpy.ndarray, type, tuple[int], str]
      """