code: def test_randint(self, seed, shape, min, max, dtype):
      np.random.seed(seed)
      np_res = np.random.randint(low=min, high=max, size=shape)
      res = random.randint(
          shape, minval=min, maxval=max, seed=seed, dtype=dtype
      )
      self.assertEqual(res.shape, shape)
      self.assertEqual(res.shape, np_res.shape)
      self.assertLessEqual(ops.max(res), max)
      self.assertGreaterEqual(ops.max(res), min)
      # Torch has incomplete dtype support for uints; will remap some dtypes.
      if keras.backend.backend() != "torch":
          self.assertEqual(backend.standardize_dtype(res.dtype), dtype)
  
imports: import numpy
  import pytest
  from absl.testing import parameterized
  import keras
  from keras import backend
  from keras import ops
  from keras import testing
  from keras.random import random
  from keras.random import seed_generator
  from keras.utils.rng_utils import set_random_seed
label_desc: Use image node_id 5 for calling the RandomTest obj's underlying member method code with example usage: obj.test_randint(seed, shape, min, max, dtype) without return types