code: def find(
      img_path: Union[str, np.ndarray],
      db_path: str,
      model_name: str = "VGG-Face",
      distance_metric: str = "cosine",
      enforce_detection: bool = True,
      detector_backend: str = "opencv",
      align: bool = True,
      normalization: str = "base",
      silent: bool = False,
  ) -> List[pd.DataFrame]:
      """
      This function applies verification several times and find the identities in a database
  
      Parameters:
              img_path: exact image path, numpy array (BGR) or based64 encoded image.
              Source image can have many faces. Then, result will be the size of number of
              faces in the source image.
  
              db_path (string): You should store some image files in a folder and pass the
              exact folder path to this. A database image can also have many faces.
              Then, all detected faces in db side will be considered in the decision.
  
              model_name (string): VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID,
              Dlib, ArcFace, SFace or Ensemble
  
              distance_metric (string): cosine, euclidean, euclidean_l2
  
              enforce_detection (bool): The function throws exception if a face could not be detected.
              Set this to False if you don't want to get exception. This might be convenient for low
              resolution images.
  
              detector_backend (string): set face detector backend to opencv, retinaface, mtcnn, ssd,
              dlib, mediapipe or yolov8.
  
              align (boolean): alignment according to the eye positions.
  
              normalization (string): normalize the input image before feeding to model
  
              silent (boolean): disable some logging and progress bars
  
      Returns:
              This function returns list of pandas data frame. Each item of the list corresponding to
              an identity in the img_path.
      """
      return recognition.find(
          img_path=img_path,
          db_path=db_path,
          model_name=model_name,
          distance_metric=distance_metric,
          enforce_detection=enforce_detection,
          detector_backend=detector_backend,
          align=align,
          normalization=normalization,
          silent=silent,
      )
  
file_path: deepface/deepface/DeepFace.py
imports: import os
  import warnings
  import logging
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  import pandas
  import tensorflow
  from deprecated import deprecated
  from deepface.commons import functions
  from deepface.commons.logger import Logger
  from deepface.modules import modeling, representation, verification, recognition, demography, detection, realtime
label_desc: Use image node_id 4 for calling a global function with example usage: find(img_path, db_path, model_name, distance_metric, enforce_detection, detector_backend, align, normalization, silent) and returns: recognition