def evaluate(self, verbose=False, decode=True, passes=None, num_threads=1, apply_experimental=True):
        """Evaluates by creating an Index containing evaluated data.

        See `LazyResult`

        Returns
        -------
        Index
            Index with evaluated data.

        """
        if self.start == 0 and self.stop == 0:
            evaluated_data = np.empty(0, dtype=np.int64)
        else:
            evaluated_data = super(Index, self).evaluate(verbose, decode, passes, num_threads, apply_experimental)

        return Index(evaluated_data, self.dtype, self.name)
def evaluate ( self , verbose = False , decode = True , passes = None , num_threads = 1 , apply_experimental = True ) : if self . start == 0 and self . stop == 0 : evaluated_data = np . empty ( 0 , dtype = np . int64 ) else : evaluated_data = super ( Index , self ) . evaluate ( verbose , decode , passes , num_threads , apply_experimental ) return Index ( evaluated_data , self . dtype , self . name )
Evaluates by creating an Index containing evaluated data.

        See `LazyResult`

        Returns
        -------
        Index
            Index with evaluated data.