class_docstr: Combiner for computing a set of dp aggregations.
  
  CompoundCombiner contains one or more combiners of other types for computing multiple metrics.
  For example it can contain [CountCombiner, SumCombiner].
  CompoundCombiner delegates all operations to the internal combiners.
  
  The type of the accumulator is a tuple that contains accumulators from internal combiners.
class_docstr_tok: ["Combiner","for","computing","a","set","of","dp","aggregations",".","CompoundCombiner","contains","one","or","more","combiners","of","other","types","for","computing","multiple","metrics",".","For","example","it","can","contain","[","CountCombiner",",","SumCombiner","]",".","CompoundCombiner","delegates","all","operations","to","the","internal","combiners",".","The","type","of","the","accumulator","is","a","tuple","that","contains","accumulators","from","internal","combiners","."]
code: def metrics_names(self) -> List[str]:
      return self._metrics_to_compute
  
imports: import abc
  import copy
  from typing import Iterable, Sized, Tuple, List
  import pipeline_dp
  import numpy
  from collections import namedtuple
label_desc: Use image node_id 5 for calling the CompoundCombiner obj's underlying member method code with example usage: obj.metrics_names() and returns: self