code: def __init__(
      self,
      params,
      num_columns: int,
      anchor_url_features: List[int],
      body_features: List[int],
      dataset_name: str = "",
      device=None,
  ):
      if "folder" not in params:
          raise Exception('Please define "folder" in "dataset"')
      self._folder = params["folder"]
      self._source_file = (
          params["source_file"]
          if "source_file" in params
          else ["train.txt"]
      )
      self._cache_file = (
          params["cache_file"] if "cache_file" in params else ""
      )
      self._num_columns = num_columns
      self._anchor_url_features = anchor_url_features
      self._body_features = body_features
  
      self._device = device
  
      self._dict = None
      self._queries = None
      self._features = None
      self._relevances = None
      self._sample_weights = None
  
      self._train_data = None
      self._validation_data = None
      self._test_data = None
  
      self._name = dataset_name
  
imports: import argparse
  import itertools
  import json
  import logging
  import os
  import pickle
  import random
  import sys
  import time
  from collections import OrderedDict
  from typing import Iterable, List, Optional, Tuple
  import numpy
  import torch
  import torch.multiprocessing
  from reagent.ope.estimators.estimator import Evaluator
  from reagent.ope.estimators.slate_estimators import DCGSlateMetric, DMEstimator, DoublyRobustEstimator, ERRSlateMetric, IPSEstimator, LogSample, NDCGSlateMetric, PassThruDistribution, PBMEstimator, PseudoInverseEstimator, RankingDistribution, RewardDistribution, SlateContext, SlateEstimator, SlateEstimatorInput, SlateItemFeatures, SlateItemValues, SlateModel, SlateQuery, SlateSlots
  from reagent.ope.estimators.types import Trainer, TrainingData
  from reagent.ope.trainers.linear_trainers import DecisionTreeTrainer, LassoTrainer
  from reagent.ope.utils import Clamper
  from torch import Tensor
label_desc: Use image node_id 1 to create a new MSLRDatasets object  with example: obj = MSLRDatasets(params, num_columns, anchor_url_features, body_features, dataset_name, device)