Extract attributes from the original dataframe used to create a tensorflow dataset

Question

I have the following dataframe df:

             sales
2015-10-05  -0.462626
2015-10-06  -0.540147
2015-10-07  -0.450222
2015-10-08  -0.448672
2015-10-09  -0.451773
... ...
2019-10-16  -0.594413
2019-10-17  -0.620770
2019-10-18  -0.586660
2019-10-19  -0.586660
2019-10-20  -0.671934
11340 rows × 1 columns

which I turn into a tf.data.Dataset like so:

data = np.array(df)
ds = tf.keras.utils.timeseries_dataset_from_array(
    data=data,
    targets=None,
    sequence_length=4,
    sequence_stride=1,
    shuffle=False,
    batch_size=1,)

The dataset gives me records looking as such

print(next(iter(ds)))
tf.Tensor(
[[[-0.4626256 ]
  [-0.54014736]
  [-0.4502221 ]
  [-0.44867167]]], shape=(1, 4, 1), dtype=float32)

Which I use for training my ML model, however, I need a way of finding the dates corresponding to the values I fetch from the dataset. Using the example fetch from the dataset above, I want to find the dates corresponding to those consecutive values, which from the dataframe we can see is [2015-10-05, 2015-10-06, 2015-10-07, 2015-10-08]. Ideally, I would like to get other attributes as well if the dataframe has several columns. Is there a way of doing so?