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: Generate CNN encodings for all images in a given directory of images. Test.
  
  Args:
      image_dir: Path to the image directory.
      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.
  
  Returns:
      dictionary: Contains a mapping of filenames and corresponding numpy array of CNN encodings.
  Example:
      ```
      from imagededup.methods import CNN
      myencoder = CNN()
      encoding_map = myencoder.encode_images(image_dir='path/to/image/directory')
      ```
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code: def encode_images(
      self,
      image_dir: Union[PurePath, str],
      recursive: Optional[bool] = False,
      num_enc_workers: int = 0,
  ) -> Dict:
      """Generate CNN encodings for all images in a given directory of images. Test.
  
      Args:
          image_dir: Path to the image directory.
          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.
  
      Returns:
          dictionary: Contains a mapping of filenames and corresponding numpy array of CNN encodings.
      Example:
          ```
          from imagededup.methods import CNN
          myencoder = CNN()
          encoding_map = myencoder.encode_images(image_dir='path/to/image/directory')
          ```
      """
      if isinstance(image_dir, str):
          image_dir = Path(image_dir)
  
      if not image_dir.is_dir():
          raise ValueError("Please provide a valid directory path!")
  
      if num_enc_workers != 0 and sys.platform != "linux":
          num_enc_workers = 0
          self.logger.info(
              f"Setting num_enc_workers to 0, CNN encoding generation parallelization support available on linux platform .."
          )
  
      return self._get_cnn_features_batch(
          image_dir=image_dir,
          recursive=recursive,
          num_workers=num_enc_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 7 for calling the CNN obj's underlying member method code with example usage: obj.encode_images(image_dir, recursive, num_enc_workers) and returns: self