code: def testMatrixConditionNumbers(self):
      for dtype in self.float_types:
          condition_number = 1000
          size = 20
  
          # Generate random positive-definite symmetric matrices, and take their
          # Eigendecomposition.
          matrix = np.random.rand(size, size)
          matrix = np.dot(matrix.T, matrix)
          _, w = np.linalg.eigh(matrix)
  
          # Build new Eigenvalues exponentially distributed between 1 and
          # 1/condition_number
          v = np.exp(
              -np.log(condition_number)
              * np.linspace(0, size, size)
              / size
          )
          matrix = np.dot(np.dot(w, np.diag(v)), w.T).astype(dtype)
          self._verifyCholesky(matrix, atol=1e-4)
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 11 for calling the CholeskyOpTest obj's underlying member method code with example usage: obj.testMatrixConditionNumbers() without return types