committing updated docs

tensorflow/core/api_def/base_api/api_def_AsString.pbtxt

@@ -42,5 +42,13 @@ Supports many numeric types and boolean.
 For Unicode, see the
 [https://www.tensorflow.org/tutorials/representation/unicode](Working with Unicode text)
 tutorial.
+
+Examples:
+```python
+>>> tf.strings.as_string([3.1415926, 2.71828])
+<tf.Tensor: id=1, shape=(2,), dtype=string, numpy=array([b'3.141593', b'2.718280'], dtype=object)>
+>>> tf.strings.as_string([3.1415926, 2.71828], precision=2)
+<tf.Tensor: id=3, shape=(2,), dtype=string, numpy=array([b'3.14', b'2.72'], dtype=object)>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_RegexFullMatch.pbtxt

@@ -26,5 +26,13 @@ The boolean values (True or False) of the output tensor indicate
 if the input matches the regex pattern provided.
 
 The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+
+Examples:
+```python
+>>> tf.strings.regex_full_match(["TensorFlow library", "Hello world"], ".*library$")
+<tf.Tensor: id=322, shape=(2,), dtype=bool, numpy=array([ True, False])>
+>>> tf.strings.regex_full_match(["TensorFlow library", "The library is called TensorFlow"], ".*library$")
+<tf.Tensor: id=324, shape=(2,), dtype=bool, numpy=array([ True, False])>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_StringJoin.pbtxt

@@ -17,5 +17,12 @@ END
   summary: "Joins the strings in the given list of string tensors into one tensor;"
   description: <<END
 with the given separator (default is an empty separator).
+
+Examples:
+```python
+>>> s = ["hello", "world", "tensorflow"]
+>>> tf.strings.join(s, " ")
+<tf.Tensor: id=11, shape=(), dtype=string, numpy=b'hello world tensorflow'>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_StringLower.pbtxt

@@ -1,3 +1,11 @@
 op {
   graph_op_name: "StringLower"
+  description: <<END
+
+Examples:
+```python
+>>> tf.strings.lower(["HELLO", "TensorFlow"])
+<tf.Tensor: id=369, shape=(2,), dtype=string, numpy=array([b'hello', b'tensorflow'], dtype=object)>
+```
+END
 }

tensorflow/core/api_def/base_api/api_def_StringStrip.pbtxt

@@ -10,6 +10,12 @@ END
     name: "output"
     description: <<END
 A string `Tensor` of the same shape as the input.
+
+Examples:
+```python
+>>> tf.strings.strip(["\nTensorFlow", "     The python library    "])
+<tf.Tensor: id=330, shape=(2,), dtype=string, numpy=array([b'TensorFlow', b'The python library'], dtype=object)>
+```
 END
   }
   summary: "Strip leading and trailing whitespaces from the Tensor."

tensorflow/core/api_def/base_api/api_def_StringToHashBucketFast.pbtxt

@@ -26,5 +26,11 @@ This function may be used when CPU time is scarce and inputs are trusted or
 unimportant. There is a risk of adversaries constructing inputs that all hash
 to the same bucket. To prevent this problem, use a strong hash function with
 `tf.string_to_hash_bucket_strong`.
+
+Examples:
+```python
+>>> tf.strings.to_hash_bucket_fast(["Hello", "TensorFlow", "2.x"], 3)
+<tf.Tensor: id=334, shape=(3,), dtype=int64, numpy=array([0, 2, 2], dtype=int64)>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_StringToHashBucketStrong.pbtxt

@@ -40,5 +40,11 @@ seeded by a high-entropy (random) "key" unknown to the adversary.
 
 The additional robustness comes at a cost of roughly 4x higher compute
 time than `tf.string_to_hash_bucket_fast`.
+
+Examples:
+```python
+>>> tf.strings.to_hash_bucket_strong(["Hello", "TensorFlow", "2.x"], 3, [1, 2])
+<tf.Tensor: id=337, shape=(3,), dtype=int64, numpy=array([2, 2, 1], dtype=int64)>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_StringToNumber.pbtxt

@@ -16,5 +16,13 @@ END
   description: <<END
 (Note that int32 overflow results in an error while float overflow
 results in a rounded value.)
+
+Examples:
+```python
+>>> tf.strings.to_number("1.55")
+<tf.Tensor: id=345, shape=(), dtype=float32, numpy=1.55>
+>>> tf.strings.to_number("3", tf.int32)
+<tf.Tensor: id=347, shape=(), dtype=int32, numpy=3>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_StringUpper.pbtxt

@@ -1,3 +1,11 @@
 op {
   graph_op_name: "StringUpper"
+  description: <<END
+
+Examples:
+```python
+>>> tf.strings.upper(["hello", "TensorFlow"])
+<tf.Tensor: id=365, shape=(2,), dtype=string, numpy=array([b'HELLO', b'TENSORFLOW'], dtype=object)>
+```
+END
 }

tensorflow/core/api_def/base_api/api_def_UnicodeScript.pbtxt

@@ -24,5 +24,11 @@ each code point. Script codes correspond to International Components for
 Unicode (ICU) UScriptCode values. See http://icu-project.org/apiref/icu4c/uscript_8h.html.
 Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will
 match input shape.
+
+Examples:
+```python
+>>> tf.strings.unicode_script([1, 31, 38])
+<tf.Tensor: id=357, shape=(3,), dtype=int32, numpy=array([0, 0, 0])>
+```
 END
 }

tensorflow/core/api_def/base_api/api_def_UnicodeTranscode.pbtxt

@@ -87,5 +87,16 @@ The end result is that if the input is marked as an explicit endianness the
 transcoding is faithful to all codepoints in the source. If it is not marked
 with an explicit endianness, the BOM is not considered part of the string itself
 but as metadata, and so is not preserved in the output.
+
+Examples:
+```python
+>>> tf.strings.unicode_transcode(["Hello", "TensorFlow", "2.x"], "UTF-8", "UTF-16-BE")
+<tf.Tensor: id=359, shape=(3,), dtype=string, numpy=
+array([b'\x00H\x00e\x00l\x00l\x00o',
+       b'\x00T\x00e\x00n\x00s\x00o\x00r\x00F\x00l\x00o\x00w',
+       b'\x002\x00.\x00x'], dtype=object)>
+>>> tf.strings.unicode_transcode(["A", "B", "C"], "US ASCII", "UTF-8")
+<tf.Tensor: id=363, shape=(3,), dtype=string, numpy=array([b'A', b'B', b'C'], dtype=object)>
+```
 END
 }

tensorflow/python/ops/ragged/ragged_string_ops.py

@@ -679,6 +679,15 @@ def ngrams(data,
   be prevented by setting `preserve_short_sequences`, which will cause the op
   to always generate at least one ngram per non-empty sequence.
 
+  Examples:
+  ```python
+  >>> tf.strings.ngrams(["TensorFlow", "and", "keras"], 2)
+  <tf.Tensor: id=285, shape=(2,), dtype=string, numpy=array([b'TensorFlow and', b'and keras'], dtype=object)>
+  >>> tf.strings.ngrams(["TensorFlow", "is", "a", "python", "library"], 3)
+  <tf.Tensor: id=320, shape=(3,), dtype=string, numpy=
+  array([b'TensorFlow is a', b'is a python', b'a python library'], dtype=object)>
+  ```
+
   Args:
     data: A Tensor or RaggedTensor containing the source data for the ngrams.
     ngram_width: The width(s) of the ngrams to create. If this is a list or

tensorflow/python/ops/string_ops.py

@@ -78,6 +78,13 @@ def regex_full_match(input, pattern, name=None):
 def regex_replace(input, pattern, rewrite, replace_global=True, name=None):
   r"""Replace elements of `input` matching regex `pattern` with `rewrite`.
 
+  Examples:
+  ```python
+  >>> tf.strings.regex_replace(["python library", "python", "Python language"], "python", "TensorFlow")
+  <tf.Tensor: id=328, shape=(3,), dtype=string, numpy=
+  array([b'TensorFlow library', b'TensorFlow', b'Python language'], dtype=object)>
+  ```
+
   Args:
     input: string `Tensor`, the source strings to process.
     pattern: string or scalar string `Tensor`, regular expression to use,
@@ -470,6 +477,14 @@ def string_to_number(input, out_type=dtypes.float32, name=None):
   (Note that int32 overflow results in an error while float overflow
   results in a rounded value.)
 
+  Examples:
+  ```python
+  >>> tf.strings.to_number("1.55")
+  <tf.Tensor: id=345, shape=(), dtype=float32, numpy=1.55>
+  >>> tf.strings.to_number("3", tf.int32)
+  <tf.Tensor: id=347, shape=(), dtype=int32, numpy=3>
+  ```
+
   Args:
     input: A `Tensor` of type `string`.
     out_type: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32,
@@ -509,6 +524,12 @@ def string_to_hash_bucket(input, num_buckets, name=None):
   This functionality will be deprecated and it's recommended to use
   `tf.strings.to_hash_bucket_fast()` or `tf.strings.to_hash_bucket_strong()`.
 
+  Examples:
+  ```python
+  >>> tf.strings.to_hash_bucket(["Hello", "TensorFlow", "2.x"], 3)
+  <tf.Tensor: id=332, shape=(3,), dtype=int64, numpy=array([2, 0, 1], dtype=int64)>
+  ```
+
   Args:
     input: A `Tensor` of type `string`.
     num_buckets: An `int` that is `>= 1`. The number of buckets.