parameters: (buffer_size, count=None, seed=None)
return_statement: return _apply_fn
docstring: Shuffles and repeats a Dataset, reshuffling with each repetition.
  
    >>> d = tf.data.Dataset.from_tensor_slices([1, 2, 3])
    >>> d = d.apply(tf.data.experimental.shuffle_and_repeat(2, count=2))
    >>> [elem.numpy() for elem in d] # doctest: +SKIP
    [2, 3, 1, 1, 3, 2]
  
    ```python
    dataset.apply(
      tf.data.experimental.shuffle_and_repeat(buffer_size, count, seed))
    ```
  
    produces the same output as
  
    ```python
    dataset.shuffle(
      buffer_size, seed=seed, reshuffle_each_iteration=True).repeat(count)
    ```
  
    In each repetition, this dataset fills a buffer with `buffer_size` elements,
    then randomly samples elements from this buffer, replacing the selected
    elements with new elements. For perfect shuffling, set the buffer size equal
    to the full size of the dataset.
  
    For instance, if your dataset contains 10,000 elements but `buffer_size` is
    set to 1,000, then `shuffle` will initially select a random element from
    only the first 1,000 elements in the buffer. Once an element is selected,
    its space in the buffer is replaced by the next (i.e. 1,001-st) element,
    maintaining the 1,000 element buffer.
  
    Args:
      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the maximum
        number elements that will be buffered when prefetching.
      count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number
        of times the dataset should be repeated. The default behavior (if `count`
        is `None` or `-1`) is for the dataset be repeated indefinitely.
      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
        seed that will be used to create the distribution. See
        `tf.random.set_seed` for behavior.
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      `tf.data.Dataset.apply`.
docstring_summary: Shuffles and repeats a Dataset, reshuffling with each repetition.
docstring_tokens: Shuffles and repeats a Dataset reshuffling with each repetition .
function: def shuffle_and_repeat(buffer_size, count=None, seed=None):
    """Shuffles and repeats a Dataset, reshuffling with each repetition.
  
    >>> d = tf.data.Dataset.from_tensor_slices([1, 2, 3])
    >>> d = d.apply(tf.data.experimental.shuffle_and_repeat(2, count=2))
    >>> [elem.numpy() for elem in d] # doctest: +SKIP
    [2, 3, 1, 1, 3, 2]
  
    ```python
    dataset.apply(
      tf.data.experimental.shuffle_and_repeat(buffer_size, count, seed))
    ```
  
    produces the same output as
  
    ```python
    dataset.shuffle(
      buffer_size, seed=seed, reshuffle_each_iteration=True).repeat(count)
    ```
  
    In each repetition, this dataset fills a buffer with `buffer_size` elements,
    then randomly samples elements from this buffer, replacing the selected
    elements with new elements. For perfect shuffling, set the buffer size equal
    to the full size of the dataset.
  
    For instance, if your dataset contains 10,000 elements but `buffer_size` is
    set to 1,000, then `shuffle` will initially select a random element from
    only the first 1,000 elements in the buffer. Once an element is selected,
    its space in the buffer is replaced by the next (i.e. 1,001-st) element,
    maintaining the 1,000 element buffer.
  
    Args:
      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the maximum
        number elements that will be buffered when prefetching.
      count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number
        of times the dataset should be repeated. The default behavior (if `count`
        is `None` or `-1`) is for the dataset be repeated indefinitely.
      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
        seed that will be used to create the distribution. See
        `tf.random.set_seed` for behavior.
  
    Returns:
      A `Dataset` transformation function, which can be passed to
      `tf.data.Dataset.apply`.
    """
  
    def _apply_fn(dataset):  # pylint: disable=missing-docstring
      return _ShuffleAndRepeatDataset(dataset, buffer_size, count, seed)
  
    return _apply_fn
function_tokens: def shuffle_and_repeat ( buffer_size , count = None , seed = None ) : def _apply_fn ( dataset ) : # pylint: disable=missing-docstring return _ShuffleAndRepeatDataset ( dataset , buffer_size , count , seed ) return _apply_fn