code: def __init__(
      self,
      normalize_embeddings=True,
      use_trunk_output=False,
      batch_size=32,
      dataloader_num_workers=2,
      pca=None,
      data_device=None,
      dtype=None,
      data_and_label_getter=None,
      label_hierarchy_level=0,
      end_of_testing_hook=None,
      dataset_labels=None,
      set_min_label_to_zero=False,
      accuracy_calculator=None,
      visualizer=None,
      visualizer_hook=None,
  ):
      self.normalize_embeddings = normalize_embeddings
      self.pca = int(pca) if pca else None
      self.use_trunk_output = use_trunk_output
      self.batch_size = int(batch_size)
      self.data_device = (
          c_f.use_cuda_if_available()
          if data_device is None
          else data_device
      )
      self.dtype = dtype
      self.dataloader_num_workers = dataloader_num_workers
      self.data_and_label_getter = (
          c_f.return_input
          if data_and_label_getter is None
          else data_and_label_getter
      )
      self.label_hierarchy_level = label_hierarchy_level
      self.end_of_testing_hook = end_of_testing_hook
      self.dataset_labels = dataset_labels
      self.set_min_label_to_zero = set_min_label_to_zero
      self.accuracy_calculator = accuracy_calculator
      self.visualizer = visualizer
      self.original_visualizer_hook = visualizer_hook
      self.initialize_label_mapper()
      self.initialize_accuracy_calculator()
      self.reference_split_names = {}
  
imports: from collections import defaultdict
  import torch
  import tqdm
  from ..utils import common_functions
  from ..utils import inference
  from ..utils.accuracy_calculator import AccuracyCalculator
label_desc: Use image node_id 1 to create a new BaseTester object  with example: obj = BaseTester(normalize_embeddings, use_trunk_output, batch_size, dataloader_num_workers, pca, data_device, dtype, data_and_label_getter, label_hierarchy_level, end_of_testing_hook, dataset_labels, set_min_label_to_zero, accuracy_calculator, visualizer, visualizer_hook)