code: def testConcurrentExecutesWithoutError(self):
      all_ops = []
      with self.session():
          for compute_v_ in True, False:
              matrix1 = random_ops.random_normal([5, 5], seed=42)
              matrix2 = random_ops.random_normal([5, 5], seed=42)
              if compute_v_:
                  e1, v1 = linalg_ops.eig(matrix1)
                  e2, v2 = linalg_ops.eig(matrix2)
                  all_ops += [e1, v1, e2, v2]
              else:
                  e1 = linalg_ops.eigvals(matrix1)
                  e2 = linalg_ops.eigvals(matrix2)
                  all_ops += [e1, e2]
          val = self.evaluate(all_ops)
          self.assertAllEqual(val[0], val[2])
          # The algorithm is slightly different for compute_v being True and False,
          # so require approximate equality only here.
          self.assertAllClose(val[2], val[4])
          self.assertAllEqual(val[4], val[5])
          self.assertAllEqual(val[1], val[3])
  
imports: import numpy
  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 gen_linalg_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 random_ops
  from tensorflow.python.ops import sort_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the EigTest obj's underlying member method code with example usage: obj.testConcurrentExecutesWithoutError() without return types