parameters: (self, dtype, dimension, distance, max_neighbors=None, search_neighborhood_size=None, num_vertices=None, level_size_decay=None)
docstring: Create object with given options.
  
          Parameters
          ----------
          dtype : EVectorComponentType
              Type of vectors.
          dimension : int
              Dimension of vectors.
          distance : EDistance
              Distance that should be used for finding nearest vectors.
          max_neighbors : int (default=32)
              Maximum number of neighbors that every item can be connected with.
          search_neighborhood_size : int (default=300)
              Search neighborhood size for ANN-search.
              Higher values improve search quality in expense of building time.
          num_vertices : int (default=0)
              Expected number of vectors in storage.
          level_size_decay : int (default=max_neighbors/2)
              Base of exponent for decaying level sizes.
docstring_summary: Create object with given options.
docstring_tokens: Create object with given options .
function: def __init__(self, dtype, dimension, distance, max_neighbors=None, search_neighborhood_size=None, num_vertices=None, level_size_decay=None):
          """
          Create object with given options.
  
          Parameters
          ----------
          dtype : EVectorComponentType
              Type of vectors.
          dimension : int
              Dimension of vectors.
          distance : EDistance
              Distance that should be used for finding nearest vectors.
          max_neighbors : int (default=32)
              Maximum number of neighbors that every item can be connected with.
          search_neighborhood_size : int (default=300)
              Search neighborhood size for ANN-search.
              Higher values improve search quality in expense of building time.
          num_vertices : int (default=0)
              Expected number of vectors in storage.
          level_size_decay : int (default=max_neighbors/2)
              Base of exponent for decaying level sizes.
          """
          self.dtype = dtype
          self.dimension = dimension
          params = {}
          all_params = ["max_neighbors", "search_neighborhood_size", "num_vertices", "level_size_decay"]
          for key, value in iteritems(locals()):
              if key in all_params and value is not None:
                  params[key] = value
          self._online_index = _OnlineHnswDenseVectorIndex[dtype](dimension, distance, json.dumps(params))
function_tokens: def __init__ ( self , dtype , dimension , distance , max_neighbors = None , search_neighborhood_size = None , num_vertices = None , level_size_decay = None ) : self . dtype = dtype self . dimension = dimension params = { } all_params = [ "max_neighbors" , "search_neighborhood_size" , "num_vertices" , "level_size_decay" ] for key , value in iteritems ( locals ( ) ) : if key in all_params and value is not None : params [ key ] = value self . _online_index = _OnlineHnswDenseVectorIndex [ dtype ] ( dimension , distance , json . dumps ( params ) )