code: def testBatch(self):
      simple_array = np.array(
          [[[1.0, 0.0], [0.0, 5.0]]]
      )  # shape (1, 2, 2)
      self._verifyCholesky(simple_array)
      self._verifyCholesky(np.vstack((simple_array, simple_array)))
      odd_sized_array = np.array(
          [[[4.0, -1.0, 2.0], [-1.0, 6.0, 0], [2.0, 0.0, 5.0]]]
      )
      self._verifyCholesky(
          np.vstack((odd_sized_array, odd_sized_array))
      )
  
      # Generate random positive-definite matrices.
      matrices = np.random.rand(10, 5, 5)
      for i in range(10):
          with self.subTest(i=i):
              matrices[i] = np.dot(matrices[i].T, matrices[i])
      self._verifyCholesky(matrices)
  
      # Generate random complex valued positive-definite matrices.
      matrices = np.random.rand(10, 5, 5) + 1j * np.random.rand(
          10, 5, 5
      )
      for i in range(10):
          with self.subTest(i=i):
              matrices[i] = np.dot(matrices[i].T.conj(), matrices[i])
      self._verifyCholesky(matrices)
  
imports: import numpy
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.linalg import linalg
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the CholeskyOpTest obj's underlying member method code with example usage: obj.testBatch() without return types