code: def __init__(self, params, device=None):
      if "folder" not in params:
          raise Exception('Please define "folder" in "dataset"')
      self._folder = params["folder"]
      self._days = params["days"] if "days" in params else []
      self._base_file_name = (
          params["base_file_name"]
          if "base_file_name" in params
          else "train"
      )
      self._min_query_count = (
          params["min_query_count"]
          if "min_query_count" in params
          else MIN_QUERY_COUNT
      )
      self._cache_file = (
          params["cache_file"] if "cache_file" in params else ""
      )
  
      self._device = device
      self._queries = None
  
      self._query_ids = None
      self._query_terms = None
      self._position_relevances = 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 TrainingDataset object  with example: obj = TrainingDataset(params, device)