code: def mahalanobis_einsum(embedding, mean, inv_covariance):
      M = embedding - mean
      distances = paddle.einsum(
          "nmhw,hwmk,nkhw->nhw", M, inv_covariance, M
      )
      distances = distances.sqrt_()
      return distances
  
imports: import os
  import argparse
  import numpy
  import matplotlib
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from tqdm import tqdm
  from skimage import measure, morphology
  from skimage.segmentation import mark_boundaries
  import paddle
label_desc: Use image node_id 5 for calling a global function with example usage: mahalanobis_einsum(embedding, mean, inv_covariance) and returns: distances