code: def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
      # Calculate evaluation metrics
      thresholds = np.arange(0, 4, 0.01)
      embeddings1 = embeddings[0::2]
      embeddings2 = embeddings[1::2]
      tpr, fpr, accuracy = calculate_roc(
          thresholds,
          embeddings1,
          embeddings2,
          np.asarray(actual_issame),
          nrof_folds=nrof_folds,
          pca=pca,
      )
      thresholds = np.arange(0, 4, 0.001)
      val, val_std, far = calculate_val(
          thresholds,
          embeddings1,
          embeddings2,
          np.asarray(actual_issame),
          1e-3,
          nrof_folds=nrof_folds,
      )
      return tpr, fpr, accuracy, val, val_std, far
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import argparse
  import sys
  import numpy
  from scipy import misc
  from sklearn.model_selection import KFold
  from scipy import interpolate
  import sklearn
  import cv2
  import math
  import datetime
  import pickle
  from sklearn.decomposition import PCA
  import mxnet
  from mxnet import ndarray
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(embeddings, actual_issame, nrof_folds, pca) and returns: tpr,  fpr,  accuracy,  val,  val_std,  far