Merge pull request #37853 from ashutosh1919:tf_dataset_map

PiperOrigin-RevId: 304478576
Change-Id: I73a15d650b99d593e83d79e5e5494e2eb181bcb8

tensorflow/python/data/ops/dataset_ops.py

@@ -1589,6 +1589,23 @@ def map(self, map_func, num_parallel_calls=None, deterministic=None):
     >>> list(d.as_numpy_iterator())
     [b'HELLO', b'WORLD']
 
+    3) Use `tf.numpy_function`, which also allows you to write arbitrary
+    Python code. Note that `tf.py_function` accepts `tf.Tensor` whereas
+    `tf.numpy_function` accepts numpy arrays and returns only numpy arrays.
+    For example:
+
+    >>> d = tf.data.Dataset.from_tensor_slices(['hello', 'world'])
+    >>> def upper_case_fn(t: np.ndarray):
+    ...   return t.decode('utf-8').upper()
+    >>> d = d.map(lambda x: tf.numpy_function(func=upper_case_fn,
+    ...           inp=[x], Tout=tf.string))
+    >>> list(d.as_numpy_iterator())
+    [b'HELLO', b'WORLD']
+
+    Note that the use of `tf.numpy_function` and `tf.py_function`
+    in general precludes the possibility of executing user-defined
+    transformations in parallel (because of Python GIL).
+
     Performance can often be improved by setting `num_parallel_calls` so that
     `map` will use multiple threads to process elements. If deterministic order
     isn't required, it can also improve performance to set