class_docstr: Find duplicates using CNN and/or generate CNN encodings given a single image or a directory of images.
  
  The module can be used for 2 purposes: Encoding generation and duplicate detection.
  - Encodings generation:
  To propagate an image through a Convolutional Neural Network architecture and generate encodings. The generated
  encodings can be used at a later time for deduplication. Using the method 'encode_image', the CNN encodings for a
  single image can be obtained while the 'encode_images' method can be used to get encodings 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.
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docstr: Take in path of the directory in which duplicates are to be detected above the given threshold.
  Returns dictionary containing key as filename and value as a list of duplicate file names.  Optionally,
  the cosine distances could be returned instead of just duplicate filenames for each query file.
  
  Args:
      image_dir: Path to the directory containing all the images.
      min_similarity_threshold: Optional, hamming distance above which retrieved duplicates are valid. Default 0.9
      scores: Optional, boolean indicating whether Hamming distances are to be returned along with retrieved
              duplicates.
      outfile: Optional, name of the file the results should be written to.
      recursive: Optional, find images recursively in a nested image directory structure, set to False by default.
      num_enc_workers: Optional, number of cpu cores to use for multiprocessing encoding generation (supported only on linux platform), set to 0 by default. 0 disables multiprocessing.
      num_sim_workers: Optional, number of cpu cores to use for multiprocessing similarity computation, set to number of CPUs in the system by default. 0 disables multiprocessing.
  
  Returns:
      if scores is True, then a dictionary of the form {'image1.jpg': [('image1_duplicate1.jpg',
      score), ('image1_duplicate2.jpg', score)], 'image2.jpg': [] ..}
      if scores is False, then a dictionary of the form {'image1.jpg': ['image1_duplicate1.jpg',
      'image1_duplicate2.jpg'], 'image2.jpg':['image1_duplicate1.jpg',..], ..}
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code: def _find_duplicates_dir(
      self,
      image_dir: Union[PurePath, str],
      min_similarity_threshold: float,
      scores: bool,
      outfile: Optional[str] = None,
      recursive: Optional[bool] = False,
      num_enc_workers: int = 0,
      num_sim_workers: int = cpu_count(),
  ) -> Dict:
      """
      Take in path of the directory in which duplicates are to be detected above the given threshold.
      Returns dictionary containing key as filename and value as a list of duplicate file names.  Optionally,
      the cosine distances could be returned instead of just duplicate filenames for each query file.
  
      Args:
          image_dir: Path to the directory containing all the images.
          min_similarity_threshold: Optional, hamming distance above which retrieved duplicates are valid. Default 0.9
          scores: Optional, boolean indicating whether Hamming distances are to be returned along with retrieved
                  duplicates.
          outfile: Optional, name of the file the results should be written to.
          recursive: Optional, find images recursively in a nested image directory structure, set to False by default.
          num_enc_workers: Optional, number of cpu cores to use for multiprocessing encoding generation (supported only on linux platform), set to 0 by default. 0 disables multiprocessing.
          num_sim_workers: Optional, number of cpu cores to use for multiprocessing similarity computation, set to number of CPUs in the system by default. 0 disables multiprocessing.
  
      Returns:
          if scores is True, then a dictionary of the form {'image1.jpg': [('image1_duplicate1.jpg',
          score), ('image1_duplicate2.jpg', score)], 'image2.jpg': [] ..}
          if scores is False, then a dictionary of the form {'image1.jpg': ['image1_duplicate1.jpg',
          'image1_duplicate2.jpg'], 'image2.jpg':['image1_duplicate1.jpg',..], ..}
      """
      self.encode_images(
          image_dir=image_dir,
          recursive=recursive,
          num_enc_workers=num_enc_workers,
      )
  
      return self._find_duplicates_dict(
          encoding_map=self.encoding_map,
          min_similarity_threshold=min_similarity_threshold,
          scores=scores,
          outfile=outfile,
          num_sim_workers=num_sim_workers,
      )
  
imports: from pathlib import Path, PurePath
  import sys
  from typing import Dict, List, Optional, Union
  import warnings
  from multiprocessing import cpu_count
  import numpy
  from PIL import Image
  import torch
  from imagededup.handlers.search.retrieval import get_cosine_similarity
  from imagededup.utils.data_generator import img_dataloader
  from imagededup.utils.models import CustomModel, MobilenetV3, DEFAULT_MODEL_NAME
  from imagededup.utils.general_utils import generate_relative_names, get_files_to_remove, save_json
  from imagededup.utils.image_utils import expand_image_array_cnn, load_image, preprocess_image
  from imagededup.utils.logger import return_logger
label_desc: Use image node_id 10 for calling the CNN obj's underlying member method code with example usage: obj._find_duplicates_dir(image_dir, min_similarity_threshold, scores, outfile, recursive, num_enc_workers, num_sim_workers) and returns: self