code: def _benchmarkRandnSampler(self, use_gpu):
      num_iters = 100
      shape = [int(1e6)]
      randn_dt, uniform_dt = randn_sampler_switchover(
          shape, num_iters, use_gpu
      )
  
      print(
          ("Randn Sampler vs uniform samplers [%d iters]\t%.4f\t%.4f")
          % (num_iters, randn_dt, uniform_dt)
      )
  
      gpu_str = "_gpu" if use_gpu else "_cpu"
      self.report_benchmark(
          name="randn_sampler" + gpu_str,
          iters=num_iters,
          wall_time=randn_dt,
      )
      self.report_benchmark(
          name="uniform_sampler" + gpu_str,
          iters=num_iters,
          wall_time=uniform_dt,
      )
  
imports: import functools
  import math
  import timeit
  import numpy
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  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 random_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 6 for calling the TruncatedNormalBenchmark obj's underlying member method code with example usage: obj._benchmarkRandnSampler(use_gpu) without return types