class_docstr: Find duplicates using hashing algorithms and/or generate hashes given a single image or a directory of images.
  
  The module can be used for 2 purposes: Encoding generation and duplicate detection.
  - Encoding generation:
  To generate hashes using specific hashing method. The generated hashes can be used at a later time for
  deduplication. Using the method 'encode_image' from the specific hashing method object, the hash for a
  single image can be obtained while the 'encode_images' method can be used to get hashes for all images in a
  directory.
  
  - Duplicate detection:
  Find duplicates either using the encoding mapping generated previously using 'encode_images' or using a Path to the
  directory that contains the images that need to be deduplicated. 'find_duplicates' and 'find_duplicates_to_remove'
  methods are provided to accomplish these tasks.
code: def _hash_func(self, image_array: np.ndarray):
      hash_mat = self._hash_algo(image_array)
      return self._array_to_hash(hash_mat)
  
imports: import os
  import sys
  import warnings
  from pathlib import PurePath, Path
  from typing import Dict, List, Optional
  from multiprocessing import cpu_count
  import numpy
  import pywt
  from scipy.fftpack import dct
  from imagededup.handlers.search.retrieval import HashEval
  from imagededup.utils.general_utils import get_files_to_remove, save_json, parallelise, generate_files, generate_relative_names
  from imagededup.utils.image_utils import load_image, preprocess_image, check_image_array_hash
  from imagededup.utils.logger import return_logger
label_desc: Use image node_id 7 for calling the Hashing obj's underlying member method code with example usage: obj._hash_func(image_array) and returns: self