message: Improve documentation for tf.data.experimental.sample_from_datasets.
  This adds a longer description and includes an example used for tf.data.experimental.sample_from_datasets (https://www.tensorflow.org/api_docs/python/tf/data/experimental/sample_from_datasets)
diff: @@ -152,6 +152,29 @@ class _DirectedInterleaveDataset(dataset_ops.DatasetV2):
  def sample_from_datasets_v2(datasets, weights=None, seed=None):
  """Samples elements at random from the datasets in `datasets`.
  + Creates a dataset by interleaving elements of `datasets` with the `weight[i]`
  + probability of picking an element from dataset `i`. For example, suppose we
  + have 2 datasets:
  +
  + ```python
  + dataset1 = tf.data.Dataset.range(0, 3)
  + dataset2 = tf.data.Dataset.range(100, 103)
  + ```
  +
  + Suppose also that we sample from these 2 datasets with the following weights:
  +
  + ```python
  + sample_dataset = tf.data.experimental.sample_from_datasets(
  + [dataset1, dataset2], weights=[0.5, 0.5])
  + ```
  +
  + One possible outcome of elements in sample_dataset is:
  +
  + ```
  + print(list(sample_dataset.as_numpy_iterator()))
  + # [100, 0, 1, 101, 2, 102]
  + ```
  +
  Args:
  datasets: A list of `tf.data.Dataset` objects with compatible structure.
  weights: (Optional.) A list of `len(datasets)` floating-point values where
  