code: def testIllConditioned(self):
      # Modified G-S handles ill-conditioned matrices much better (numerically)
      # than original G-S.
      dim = 200
      mat = tf.eye(200, dtype=tf.float64) * 1e-5 + scipy_linalg.hilbert(
          dim
      )
      mat = tf.math.l2_normalize(mat, axis=-1)
      ortho = gram_schmidt(mat)
      xtx = tf.matmul(ortho, ortho, transpose_a=True)
      self.assertAllClose(
          0.0,
          tf.linalg.norm(
              tf.eye(dim, dtype=tf.float64) - xtx,
              ord="fro",
              axis=(-1, -2),
          ),
      )
  
file_path: probability/tensorflow_probability/python/math/gram_schmidt_test.py
imports: import numpy
  import scipy.linalg
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.gram_schmidt import gram_schmidt
label_desc: Use image node_id 3 for calling the GramSchmidtTest obj's underlying member method code with example usage: obj.testIllConditioned() without return types