class_docstr: Complements the event system by providing aggregation functionality. How the events are aggregated
  is specified by the event interface decorators (see event_decorators.py).
  
  Note that the statistics calculation for episode aggregators will automatically be
  triggered on every `increment_log_step()` call.
class_docstr_tok: ["Complements","the","event","system","by","providing","aggregation","functionality",".","How","the","events","are","aggregated","is","specified","by","the","event","interface","decorators","(","see","event_decorators.py",")",".","Note","that","the","statistics","calculation","for","episode","aggregators","will","automatically","be","triggered","on","every","`","increment_log_step","(",")","`","call","."]
docstr: Clear the input statistics (and start fresh).
docstr_tok: ["Clear","the","input","statistics","(","and","start","fresh",")","."]
code: def clear_inputs(self) -> None:
      """Clear the input statistics (and start fresh)."""
      self.input: Dict[LogStatsKey, list] = defaultdict(list)
  
imports: from abc import ABC, abstractmethod
  from collections import defaultdict
  from enum import Enum
  from typing import Union, Callable, Dict, Optional, NamedTuple, List, Type, TypeVar, Tuple, Any
  import numpy
  from maze.core.events.event_record import EventRecord
  from maze.core.events.event_topic_factory import event_topic_factory
label_desc: Use image node_id 3 for calling the LogStatsAggregator obj's underlying member method code with example usage: obj.clear_inputs() without return types