code: def testUniformInts(self):
      minv = -2
      maxv = 15
      n = 100000
      p = 1 / (maxv - minv)
      # The counts should follow an (n, p) binomial distribution.
      mean = p * n
      std = np.sqrt(n * p * (1 - p))
      for dt in dtypes.int32, dtypes.int64:
          # Use a fixed seed here to make the test deterministic.
          # Without the fixed seed, the 5 * std bound will (very rarely) fail.
          sampler = self._Sampler(
              n // 10,
              minv=minv,
              maxv=maxv,
              dtype=dt,
              use_gpu=True,
              seed=17,
          )
          x = sampler().ravel()
          self.assertEqual(x.shape, (n,))
          counts, _ = np.histogram(x, bins=maxv - minv)
          self.assertEqual(counts.shape, (maxv - minv,))
          self.assertEqual(counts.sum(), n)
          error = np.abs(counts - mean)
          self.assertLess(error.max(), 5 * std)
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  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 gen_random_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the RandomUniformTest obj's underlying member method code with example usage: obj.testUniformInts() without return types