code: def _benchmark_bitwise_and(self, mat, device):
      if device == GPU and not context.num_gpus():
          return
      with context.device(device):
          if device == GPU:
              mat = mat.gpu()
          func = lambda: bitwise_ops.bitwise_and(mat, mat)
          self._run(func, num_iters=5000)
  
imports: import time
  from tensorflow.python.data.experimental.ops import prefetching_ops
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import benchmarks_test_base
  from tensorflow.python.eager import context
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import bitwise_ops
  from tensorflow.python.ops import critical_section_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_map_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 5 for calling the MicroBenchmarks obj's underlying member method code with example usage: obj._benchmark_bitwise_and(mat, device) without return types