message: Add additional example to timeseries_dataset_from_array
diff: @@ -99,22 +99,48 @@ def timeseries_dataset_from_array(
  can be generated to include them (the next sequence would have started
  at index 81, and thus its last step would have gone over 99).
  - Example 2: temporal regression. Consider an array `data` of scalar
  - values, of shape `(steps,)`. To generate a dataset that uses the past 10
  + Example 2: temporal regression.
  + Consider an array `data` of scalar values, of shape `(steps,)`.
  + To generate a dataset that uses the past 10
  timesteps to predict the next timestep, you would use:
  ```python
  - input_data = data
  - offset = 10
  - targets = data[offset:]
  + input_data = data[:-10]
  + targets = data[10:]
  dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
  - input_data, targets, sequence_length=offset)
  + input_data, targets, sequence_length=10)
  for batch in dataset:
  inputs, targets = batch
  assert np.array_equal(inputs[0], data[:10]) # First sequence: steps [0-9]
  assert np.array_equal(targets[0], data[10]) # Corresponding target: step 10
  break
  ```
  +
  + Example 3: temporal regression for many-to-many architectures.
  + Consider two arrays of scalar values `X` and `Y`,
  + both of shape `(100,)`. The resulting dataset should consist samples with
  + 20 timestamps each. The samples should not overlap.
  + To generate a dataset that uses the current timestamp
  + to predict the corresponding target timestep, you would use:
  +
  + ```python
  + X = np.arange(100)
  + Y = X*2
  +
  + sample_length = 20
  + input_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
  + X, None, sequence_length=sample_length, sequence_stride=sample_length)
  + target_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
  + Y, None, sequence_length=sample_length, sequence_stride=sample_length)
  +
  + for batch in zip(input_dataset, target_dataset):
  + inputs, targets = batch
  + assert np.array_equal(inputs[0], X[:sample_length])
  +
  + # second sample equals output timestamps 20-40
  + assert np.array_equal(targets[1], Y[sample_length:2*sample_length])
  + break
  + ```
  """
  if start_index and (start_index < 0 or start_index >= len(data)):
  raise ValueError('start_index must be higher than 0 and lower than the '
  