code: def run_model_test_1N(self, npoints=100):
      fars_cal = [10**ii for ii in np.arange(-4, 0, 4 / npoints)] + [
          1
      ]  # plot in range [10-4, 1]
      fars_show_idx = np.arange(len(fars_cal))[
          :: npoints // 4
      ]  # npoints=100, fars_show=[0.0001, 0.001, 0.01, 0.1, 1.0]
  
      (
          g1_templates,
          g1_ids,
          g2_templates,
          g2_ids,
          probe_mixed_templates,
          probe_mixed_ids,
      ) = extract_gallery_prob_data(
          self.data_path, self.subset, force_reload=self.force_reload
      )
      img_input_feats = process_embeddings(
          self.embs,
          self.embs_f,
          use_flip_test=True,
          use_norm_score=False,
          use_detector_score=True,
          face_scores=self.face_scores,
      )
      (
          g1_templates_feature,
          g1_unique_templates,
          g1_unique_ids,
      ) = image2template_feature(
          img_input_feats,
          self.templates,
          self.medias,
          g1_templates,
          g1_ids,
      )
      (
          g2_templates_feature,
          g2_unique_templates,
          g2_unique_ids,
      ) = image2template_feature(
          img_input_feats,
          self.templates,
          self.medias,
          g2_templates,
          g2_ids,
      )
      (
          probe_mixed_templates_feature,
          probe_mixed_unique_templates,
          probe_mixed_unique_subject_ids,
      ) = image2template_feature(
          img_input_feats,
          self.templates,
          self.medias,
          probe_mixed_templates,
          probe_mixed_ids,
      )
      print(
          "g1_templates_feature:", g1_templates_feature.shape
      )  # (1772, 512)
      print(
          "g2_templates_feature:", g2_templates_feature.shape
      )  # (1759, 512)
  
      print(
          "probe_mixed_templates_feature:",
          probe_mixed_templates_feature.shape,
      )  # (19593, 512)
      print(
          "probe_mixed_unique_subject_ids:",
          probe_mixed_unique_subject_ids.shape,
      )  # (19593,)
  
      print(">>>> Gallery 1")
      (
          g1_top_1_count,
          g1_top_5_count,
          g1_top_10_count,
          g1_threshes,
          g1_recalls,
          g1_cmc_scores,
      ) = evaluation_1N(
          probe_mixed_templates_feature,
          g1_templates_feature,
          probe_mixed_unique_subject_ids,
          g1_unique_ids,
          fars_cal,
      )
      print(">>>> Gallery 2")
      (
          g2_top_1_count,
          g2_top_5_count,
          g2_top_10_count,
          g2_threshes,
          g2_recalls,
          g2_cmc_scores,
      ) = evaluation_1N(
          probe_mixed_templates_feature,
          g2_templates_feature,
          probe_mixed_unique_subject_ids,
          g2_unique_ids,
          fars_cal,
      )
      print(">>>> Mean")
      query_num = probe_mixed_templates_feature.shape[0]
      top_1 = (g1_top_1_count + g2_top_1_count) / query_num
      top_5 = (g1_top_5_count + g2_top_5_count) / query_num
      top_10 = (g1_top_10_count + g2_top_10_count) / query_num
      print(
          "[Mean] top1: %f, top5: %f, top10: %f"
          % (top_1, top_5, top_10)
      )
  
      mean_tpirs = (np.array(g1_recalls) + np.array(g2_recalls)) / 2
      show_result = {}
      for id, far in enumerate(fars_cal):
          if id in fars_show_idx:
              show_result.setdefault("far", []).append(far)
              show_result.setdefault("g1_tpir", []).append(
                  g1_recalls[id]
              )
              show_result.setdefault("g1_thresh", []).append(
                  g1_threshes[id]
              )
              show_result.setdefault("g2_tpir", []).append(
                  g2_recalls[id]
              )
              show_result.setdefault("g2_thresh", []).append(
                  g2_threshes[id]
              )
              show_result.setdefault("mean_tpir", []).append(
                  mean_tpirs[id]
              )
      print(pd.DataFrame(show_result).set_index("far").to_markdown())
      return fars_cal, mean_tpirs, g1_cmc_scores, g2_cmc_scores
  
imports: import os
  import cv2
  import numpy
  import pandas
  from tqdm import tqdm
  from skimage import transform
  from sklearn.preprocessing import normalize
  from sklearn.metrics import roc_curve, auc
label_desc: Use image node_id 4 for calling the IJB_test obj's underlying member method code with example usage: obj.run_model_test_1N(npoints) and returns: fars_cal,  mean_tpirs,  g1_cmc_scores,  g2_cmc_scores