message: [tf.data] Add more detail and examples to make_csv_dataset documentation.
diff: @@ -343,11 +343,53 @@ def make_csv_dataset_v2(
  ):
  """Reads CSV files into a dataset.
  - Reads CSV files into a dataset, where each element is a (features, labels)
  - tuple that corresponds to a batch of CSV rows. The features dictionary
  - maps feature column names to `Tensor`s containing the corresponding
  + Reads CSV files into a dataset, where each element of the dataset is a
  + (features, labels) tuple that corresponds to a batch of CSV rows. The features
  + dictionary maps feature column names to `Tensor`s containing the corresponding
  feature data, and labels is a `Tensor` containing the batch's label data.
  + By default, the first rows of the CSV files are expected to be headers listing
  + the column names. If the first rows are not headers, set `header=False` and
  + provide the column names with the `column_names` argument.
  +
  + By default, the dataset is repeated indefinitely, reshuffling the order each
  + time. This behavior can be modified by setting the `num_epochs` and `shuffle`
  + arguments.
  +
  + For example, suppose you have a CSV file containing
  +
  + | Feature_A | Feature_B |
  + | --------- | --------- |
  + | 1 | "a" |
  + | 2 | "b" |
  + | 3 | "c" |
  + | 4 | "d" |
  +
  + ```
  + # No label column specified
  + dataset = tf.data.experimental.make_csv_dataset(filename, batch_size=2)
  + iterator = ds.as_numpy_iterator()
  + print(dict(next(iterator)))
  + # prints a dictionary of batched features:
  + # OrderedDict([('Feature_A', array([1, 4], dtype=int32)),
  + # ('Feature_B', array([b'a', b'd'], dtype=object))])
  + ```
  +
  + ```
  + # Set Feature_B as label column
  + dataset = tf.data.experimental.make_csv_dataset(
  + filename, batch_size=2, label_name="Feature_B")
  + iterator = ds.as_numpy_iterator()
  + print(next(iterator))
  + # prints (features, labels) tuple:
  + # (OrderedDict([('Feature_A', array([1, 2], dtype=int32))]),
  + # array([b'a', b'b'], dtype=object))
  + ```
  +
  + See the
  + [Load CSV data guide](https://www.tensorflow.org/tutorials/load_data/csv) for
  + more examples of using `make_csv_dataset` to read CSV data.
  +
  Args:
  file_pattern: List of files or patterns of file paths containing CSV
  records. See `tf.io.gfile.glob` for pattern rules.
  