code: def svd_orthogonal(fin, fout, use_paddle=False):
      assert fin > fout, "fin > fout"
      if use_paddle:
          X = paddle.rand((fout, fin))
          U, _, Vt = paddle.linalg.svd(X, full_matrices=False)
          # print(Vt.shape)
          # print(paddle.allclose(Vt@Vt.T, paddle.eye(Vt.shape[0])))
      else:
          X = np.random.random((fout, fin))
          U, _, Vt = np.linalg.svd(X, full_matrices=False)
          # print(Vt.shape)
          # print(np.allclose((Vt@ Vt.T), np.eye(Vt.shape[0])))
      W = paddle.to_tensor(Vt, dtype=paddle.float32).T
      return W
  
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 6 for calling a global function with example usage: svd_orthogonal(fin, fout, use_paddle) and returns: W