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: Apply preprocessing function for mobilenet to images.
  
  Args:
      im_arr: Image typecast to numpy array.
  
  Returns:
      transformed_image_tensor: Transformed images returned as a pytorch tensor.
docstr_tok: ["Apply","preprocessing","function","for","mobilenet","to","images",".","Args",":","im_arr",":","Image","typecast","to","numpy","array",".","Returns",":","transformed_image_tensor",":","Transformed","images","returned","as","a","pytorch","tensor","."]
code: def apply_preprocess(self, im_arr: np.array) -> torch.tensor:
      """
      Apply preprocessing function for mobilenet to images.
  
      Args:
          im_arr: Image typecast to numpy array.
  
      Returns:
          transformed_image_tensor: Transformed images returned as a pytorch tensor.
      """
      image_pil = Image.fromarray(im_arr)
      return self.transform(image_pil)
  
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 3 for calling the CNN obj's underlying member method code with example usage: obj.apply_preprocess(im_arr) and returns: self