code: def __init__(self, cfg, args, nprobe, index_factory_str):
      self.input_d = cfg["d"]
      self.dt = cfg["datasets"][args.xb]["files"][0]["dtype"]
      assert self.input_d > 0
      output_dir = cfg["output"]
      assert os.path.exists(output_dir)
      self.index_factory = index_factory_str
      assert self.index_factory is not None
      self.index_factory_fn = self.index_factory.replace(",", "_")
      self.index_template_file = f"{output_dir}/{args.xb}/{self.index_factory_fn}.empty.faissindex"
      logging.info(f"index template: {self.index_template_file}")
  
      if not args.xq:
          args.xq = args.xb
  
      self.by_residual = True
      if args.no_residuals:
          self.by_residual = False
  
      xb_output_dir = f"{output_dir}/{args.xb}"
      if not os.path.exists(xb_output_dir):
          os.makedirs(xb_output_dir)
      xq_output_dir = f"{output_dir}/{args.xq}"
      if not os.path.exists(xq_output_dir):
          os.makedirs(xq_output_dir)
      search_output_dir = f"{output_dir}/{args.xq}_in_{args.xb}"
      if not os.path.exists(search_output_dir):
          os.makedirs(search_output_dir)
      self.knn_dir = f"{search_output_dir}/knn"
      if not os.path.exists(self.knn_dir):
          os.makedirs(self.knn_dir)
      self.eval_dir = f"{search_output_dir}/eval"
      if not os.path.exists(self.eval_dir):
          os.makedirs(self.eval_dir)
      self.index = {}  # to keep a reference to opened indices,
      self.ivls = {}  # hstack inverted lists,
      self.index_shards = {}  # and index shards
      self.index_shard_prefix = (
          f"{xb_output_dir}/{self.index_factory_fn}.shard_"
      )
      self.xq_index_shard_prefix = (
          f"{xq_output_dir}/{self.index_factory_fn}.shard_"
      )
      self.index_file = (  # TODO: added back temporarily for evaluate, handle name of non-sharded index file and remove.
          f"{xb_output_dir}/{self.index_factory_fn}.faissindex"
      )
      self.xq_index_file = (
          f"{xq_output_dir}/{self.index_factory_fn}.faissindex"
      )
      self.training_sample = cfg["training_sample"]
      self.evaluation_sample = cfg["evaluation_sample"]
      self.xq_ds = create_dataset_from_oivf_config(cfg, args.xq)
      self.xb_ds = create_dataset_from_oivf_config(cfg, args.xb)
      file_descriptors = self.xq_ds.file_descriptors
      self.file_sizes = [fd.size for fd in file_descriptors]
      self.shard_size = cfg["index_shard_size"]  # ~100GB
      self.nshards = self.xb_ds.size // self.shard_size
      if self.xb_ds.size % self.shard_size != 0:
          self.nshards += 1
      self.xq_nshards = self.xq_ds.size // self.shard_size
      if self.xq_ds.size % self.shard_size != 0:
          self.xq_nshards += 1
      self.nprobe = nprobe
      assert self.nprobe > 0, "Invalid nprobe parameter."
      if "deduper" in cfg:
          self.deduper = cfg["deduper"]
          self.deduper_codec_fn = [
              f"{xb_output_dir}/deduper_codec_{codec.replace(',', '_')}"
              for codec in self.deduper
          ]
          self.deduper_idx_fn = [
              f"{xb_output_dir}/deduper_idx_{codec.replace(',', '_')}"
              for codec in self.deduper
          ]
      else:
          self.deduper = None
      self.k = cfg["k"]
      assert self.k > 0, "Invalid number of neighbours parameter."
      self.knn_output_file_suffix = (
          f"{self.index_factory_fn}_np{self.nprobe}.npy"
      )
  
      fp = 32
      if self.dt == "float16":
          fp = 16
  
      self.xq_bs = cfg["query_batch_size"]
      if "metric" in cfg:
          self.metric = eval(f'faiss.{cfg["metric"]}')
      else:
          self.metric = faiss.METRIC_L2
  
      if "evaluate_by_margin" in cfg:
          self.evaluate_by_margin = cfg["evaluate_by_margin"]
      else:
          self.evaluate_by_margin = False
  
      os.system("grep -m1 'model name' < /proc/cpuinfo")
      os.system("grep -E 'MemTotal|MemFree' /proc/meminfo")
      os.system("nvidia-smi")
      os.system("nvcc --version")
  
      self.knn_queries_memory_limit = 4 * 1024 * 1024 * 1024  # 4 GB
      self.knn_vectors_memory_limit = 8 * 1024 * 1024 * 1024
  
imports: import faiss
  import numpy
  import os
  from tqdm import tqdm, trange
  import sys
  import logging
  from faiss.contrib.ondisk import merge_ondisk
  from faiss.contrib.big_batch_search import big_batch_search
  from faiss.contrib.exhaustive_search import knn_ground_truth
  from faiss.contrib.evaluation import knn_intersection_measure
  from utils import get_intersection_cardinality_frequencies, margin, is_pretransform_index
  from dataset import create_dataset_from_oivf_config
label_desc: Use image node_id 1 to create a new OfflineIVF object  with example: obj = OfflineIVF(cfg, args, nprobe, index_factory_str)