class_docstr: Microbenchmarks for the stateless random ops.
class_docstr_tok: ["Microbenchmarks","for","the","stateless","random","ops","."]
code: def _benchmarkUniform(self, name, dtype, use_xla_jit):
      def builder_fn():
          shape = (10, 1000, 1000)
          seed_var = variables.Variable(
              (312, 456), dtype=dtypes.int32, name="input"
          )
          random_t = stateless.stateless_random_uniform(
              shape, seed=seed_var, dtype=dtype
          )
          return "%s.shape%s" % (name, shape), [random_t]
  
      xla_test.Benchmark(
          self, builder_fn, use_xla_jit=use_xla_jit, device="cpu"
      )
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.client import device_lib
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import config
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.kernel_tests.random import util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the StatelessRandomOpsBenchmark obj's underlying member method code with example usage: obj._benchmarkUniform(name, dtype, use_xla_jit) and returns: unknown,  list