code: def forward(self, imgs):
      if not isinstance(imgs, list):
          imgs = [imgs]
      input_size = self.image_size
      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] != input_size[1]
                  or nimg.shape[1] != input_size[0]
              ):
                  nimg = cv2.resize(nimg, input_size)
              nimgs.append(nimg)
          imgs = nimgs
      blob = cv2.dnn.blobFromImages(
          imgs,
          1.0 / self.input_std,
          input_size,
          (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 5 for calling the ArcFaceORT obj's underlying member method code with example usage: obj.forward(imgs) and returns: net_out