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","."]
code: def aggregate(self, values):
      for i, action in enumerate(self.actions):
          self.values[action].append((values == i).sum().item())
  
      latest_count = {
          action: counts[-1] for action, counts in self.values.items()
      }
      logger.info(f"{self.key} {latest_count}")
  
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 2 for calling the ActionCountAggregator obj's underlying member method code with example usage: obj.aggregate(values) without return types