edited flat_map description and removed typo

tensorflow/python/data/ops/dataset_ops.py

@@ -1007,8 +1007,25 @@ def i(...):
       return ParallelMapDataset(self, map_func, num_parallel_calls)
 
   def flat_map(self, map_func):
-    """Maps `map_func` across this dataset and flattens the result.
+    """Maps `map_func` across this dataset and flattens the result. 
+
+    Use `flat_map` if you want to make sure that the order of your dataset
+    stays the same. For example, to flatten a dataset of batches into a
+    dataset of their elements:
 
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset. '[...]' represents a tensor.
+    a = {[1,2,3,4,5], [6,7,8,9], [10]}
+
+    a.flat_map(lambda x: Dataset.from_tensor_slices(x)) == 
+      {[1,2,3,4,5,6,7,8,9,10]}
+    ```
+
+    `tf.data.Dataset.interleave()` is a generalization of `flat_map`, since 
+    `flat_map` produces the same output as 
+    `tf.data.Dataset.interleave(cycle_length=1)`
+
     Args:
       map_func: A function mapping a nested structure of tensors (having shapes
         and types defined by `self.output_shapes` and `self.output_types`) to a
@@ -1043,7 +1060,7 @@ def interleave(self,
     elements are produced. `cycle_length` controls the number of input elements
     that are processed concurrently. If you set `cycle_length` to 1, this
     transformation will handle one input element at a time, and will produce
-    identical results = to `tf.data.Dataset.flat_map`. In general,
+    identical results to `tf.data.Dataset.flat_map`. In general,
     this transformation will apply `map_func` to `cycle_length` input elements,
     open iterators on the returned `Dataset` objects, and cycle through them
     producing `block_length` consecutive elements from each iterator, and