class_docstr: Counting the frequency of each action. Actions are indexed from `0` to
  `len(actions) - 1`. The input is assumed to contain action index.
class_docstr_tok: ["Counting","the","frequency","of","each","action",".","Actions","are","indexed","from","`","0","`","to","`","len","(","actions",")","-","1","`",".","The","input","is","assumed","to","contain","action","index","."]
docstr: Returns the action disributions in each aggregating step
docstr_tok: ["Returns","the","action","disributions","in","each","aggregating","step"]
code: def get_distributions(self) -> Dict[str, List[float]]:
      """
      Returns the action disributions in each aggregating step
      """
      totals = np.array(
          [sum(counts) for counts in zip(*self.values.values())]
      )
      return {
          action: (np.array(counts) / totals).tolist()
          for action, counts in self.values.items()
      }
  
imports: import logging
  from collections import deque
  from typing import Any, Callable, Deque, Dict, List, Optional
  import numpy
  import torch
  from reagent.core.tensorboardX import SummaryWriterContext
  from reagent.core.tracker import Aggregator
label_desc: Use image node_id 3 for calling the ActionCountAggregator obj's underlying member method code with example usage: obj.get_distributions() and returns: unknown