code: def __init__(
      self,
      user_id: int,
      query_id: int,
      query_terms: Tuple[int],
      list: Sequence[Tuple[int, int]],
  ):
      self._user_id = user_id
      self._query_id = query_id
      self._query_terms = query_terms
      self._list = list
      self._clicks: List[Tuple[int, int]] = []
      self._position_relevances: Optional[List[float]] = None
      self._url_relevances: Optional[
          MutableMapping[Tuple[int, int], float]
      ] = None
  
imports: import argparse
  import json
  import logging
  import os
  import pickle
  import random
  import sys
  import time
  from typing import Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple
  import numpy
  import torch
  import torch.multiprocessing
  from reagent.ope.estimators.estimator import Evaluator
  from reagent.ope.estimators.slate_estimators import DCGSlateMetric, ERRSlateMetric, FrechetDistribution, IPSEstimator, LogSample, NDCGSlateMetric, PBMEstimator, PseudoInverseEstimator, RankingDistribution, RewardDistribution, SlateContext, SlateEstimator, SlateEstimatorInput, SlateItemValues, SlateModel, SlateQuery, SlateSlots, SlateSlotValues
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 1 to create a new LoggedQuery object  with example: obj = LoggedQuery(user_id, query_id, query_terms, list)