code: def check_batch(self, img):
      if not isinstance(img, list):
          imgs = [
              img,
          ] * 32
      if self.crop is not None:
          nimgs = []
          for img in imgs:
              nimg = img[
                  self.crop[1] : self.crop[3],
                  self.crop[0] : self.crop[2],
                  :,
              ]
              if (
                  nimg.shape[0] != self.image_size[1]
                  or nimg.shape[1] != self.image_size[0]
              ):
                  nimg = cv2.resize(nimg, self.image_size)
              nimgs.append(nimg)
          imgs = nimgs
      blob = cv2.dnn.blobFromImages(
          images=imgs,
          scalefactor=1.0 / self.input_std,
          size=self.image_size,
          mean=(self.input_mean, self.input_mean, self.input_mean),
          swapRB=True,
      )
      net_out = self.session.run(
          self.output_names, {self.input_name: blob}
      )[0]
      return net_out
  
file_path: insightface/recognition/arcface_torch/onnx_helper.py
imports: from __future__ import division
  import datetime
  import os
  import os.path
  import glob
  import numpy
  import cv2
  import sys
  import onnxruntime
  import onnx
  import argparse
  from onnx import numpy_helper
  from insightface.data import get_image
label_desc: Use image node_id 3 for calling the ArcFaceORT obj's underlying member method code with example usage: obj.check_batch(img) and returns: net_out