code: def _adjust_scores(self, scores: Scores, batch_edges: EdgeList):
      # This is a hook for the filtered evaluator to do the filtering
      # of true edges
      pass
  
imports: import argparse
  import logging
  import time
  from functools import partial
  from typing import Callable, Generator, List, Optional, Tuple
  import torch
  from torchbiggraph.batching import AbstractBatchProcessor, call, process_in_batches
  from torchbiggraph.bucket_scheduling import create_buckets_ordered_lexicographically
  from torchbiggraph.checkpoint_manager import CheckpointManager
  from torchbiggraph.config import add_to_sys_path, ConfigFileLoader, ConfigSchema
  from torchbiggraph.edgelist import EdgeList
  from torchbiggraph.graph_storages import EDGE_STORAGES
  from torchbiggraph.losses import LOSS_FUNCTIONS
  from torchbiggraph.model import make_model, MultiRelationEmbedder, Scores
  from torchbiggraph.stats import average_of_sums, Stats
  from torchbiggraph.types import Bucket, EntityName, Partition, UNPARTITIONED
  from torchbiggraph.util import compute_randomized_auc, create_pool, EmbeddingHolder, get_async_result, get_num_workers, set_logging_verbosity, setup_logging, split_almost_equally, SubprocessInitializer, tag_logs_with_process_name
label_desc: Use image node_id 2 for calling the RankingEvaluator obj's underlying member method code with example usage: obj._adjust_scores(scores, batch_edges) without return types