class_docstr: A `Dataset` of pseudorandom values.
class_docstr_tok: ["A","`","Dataset","`","of","pseudorandom","values","."]
docstr: A `Dataset` of pseudorandom values.
docstr_tok: ["A","`","Dataset","`","of","pseudorandom","values","."]
code: def __init__(
      self, seed=None, rerandomize_each_iteration=None, name=None
  ):
      """A `Dataset` of pseudorandom values."""
      self._seed, self._seed2 = random_seed.get_seed(seed)
      self._rerandomize = rerandomize_each_iteration
      self._name = name
      if rerandomize_each_iteration:
          if not tf2.enabled():
              warnings.warn(
                  "In TF 1, the `rerandomize_each_iteration=True` option "
                  "is only supported for repeat-based epochs."
              )
          variant_tensor = ged_ops.random_dataset_v2(
              seed=self._seed,
              seed2=self._seed2,
              seed_generator=gen_dataset_ops.dummy_seed_generator(),
              rerandomize_each_iteration=self._rerandomize,
              **self._common_args,
          )
      else:
          variant_tensor = ged_ops.random_dataset(
              seed=self._seed, seed2=self._seed2, **self._common_args
          )
      super().__init__(variant_tensor)
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/random_op.py
imports: import warnings
  from tensorflow.python import tf2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import random_seed
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import gen_dataset_ops
  from tensorflow.python.ops import gen_experimental_dataset_ops
label_desc: Use image node_id 1 to create a new _RandomDataset object from inherited base classes: dataset_ops with example: obj = _RandomDataset(seed, rerandomize_each_iteration, name)