code: def test1DLarge(self):
      # This test ensures that the operation is correct even when the naive
      # implementation would overflow.
      x_np = np.arange(20) * 20.0
  
      for use_gpu in (True, False):
          with self.cached_session(use_gpu=use_gpu):
              x_tf = ops.convert_to_tensor(x_np, dtype=dtypes.float32)
  
              result_fused = self.evaluate(
                  math_ops.cumulative_logsumexp(x_tf)
              )
              result_map = self.evaluate(self._logSumExpMap(x_tf))
  
          self.assertAllClose(result_fused, result_map)
  
file_path: tensorflow/tensorflow/python/kernel_tests/math_ops/cumulative_logsumexp_test.py
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 10 for calling the CumulativeLogsumexpTest obj's underlying member method code with example usage: obj.test1DLarge() without return types