Export StringLookup and IntegerLookup layers.

PiperOrigin-RevId: 313801697
Change-Id: Ib159a0b7fe36e6d9d00a7e2d6bc6fbeb3c76af10

tensorflow/python/keras/layers/__init__.py

@@ -47,19 +47,31 @@ if tf2.enabled():
   from tensorflow.python.keras.layers.preprocessing.category_encoding import CategoryEncoding
   from tensorflow.python.keras.layers.preprocessing.category_encoding_v1 import CategoryEncoding as CategoryEncodingV1
   CategoryEncodingV2 = CategoryEncoding
+  from tensorflow.python.keras.layers.preprocessing.integer_lookup import IntegerLookup
+  from tensorflow.python.keras.layers.preprocessing.integer_lookup_v1 import IntegerLookup as IntegerLookupV1
+  IntegerLookupV2 = IntegerLookup
   from tensorflow.python.keras.layers.preprocessing.normalization import Normalization
   from tensorflow.python.keras.layers.preprocessing.normalization_v1 import Normalization as NormalizationV1
   NormalizationV2 = Normalization
+  from tensorflow.python.keras.layers.preprocessing.string_lookup import StringLookup
+  from tensorflow.python.keras.layers.preprocessing.string_lookup_v1 import StringLookup as StringLookupV1
+  StringLookupV2 = StringLookup
   from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization
   from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization as TextVectorizationV1
   TextVectorizationV2 = TextVectorization
 else:
+  from tensorflow.python.keras.layers.preprocessing.integer_lookup_v1 import IntegerLookup
+  from tensorflow.python.keras.layers.preprocessing.integer_lookup import IntegerLookup as IntegerLookupV2
+  IntegerLookupV1 = IntegerLookup
   from tensorflow.python.keras.layers.preprocessing.category_encoding_v1 import CategoryEncoding
   from tensorflow.python.keras.layers.preprocessing.category_encoding import CategoryEncoding as CategoryEncodingV2
   CategoryEncodingV1 = CategoryEncoding
   from tensorflow.python.keras.layers.preprocessing.normalization_v1 import Normalization
   from tensorflow.python.keras.layers.preprocessing.normalization import Normalization as NormalizationV2
   NormalizationV1 = Normalization
+  from tensorflow.python.keras.layers.preprocessing.string_lookup_v1 import StringLookup
+  from tensorflow.python.keras.layers.preprocessing.string_lookup import StringLookup as StringLookupV2
+  StringLookupV1 = StringLookup
   from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization
   from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization as TextVectorizationV2
   TextVectorizationV1 = TextVectorization

tensorflow/python/keras/layers/preprocessing/integer_lookup.py

@@ -20,8 +20,10 @@ from __future__ import print_function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.keras.layers.preprocessing import index_lookup
 from tensorflow.python.keras.layers.preprocessing import table_utils
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export("keras.layers.experimental.preprocessing.IntegerLookup", v1=[])
 class IntegerLookup(index_lookup.IndexLookup):
   """Maps integers from a vocabulary to integer indices.
 
@@ -39,18 +41,18 @@ class IntegerLookup(index_lookup.IndexLookup):
   Attributes:
     max_values: The maximum size of the vocabulary for this layer. If None,
       there is no cap on the size of the vocabulary. Note that this vocabulary
-      includes the OOV and mask tokens, so the effective number of tokens is
-      (max_tokens - num_oov_tokens - (1 if mask_token else 0))
+      includes the OOV and mask values, so the effective number of values is
+      (max_values - num_oov_values - (1 if mask_token else 0))
     num_oov_indices: The number of out-of-vocabulary values to use; defaults to
-      1. If this value is more than 1, OOV inputs are hashed to determine their
-      OOV value; if this value is 0, passing an OOV input will result in a '-1'
-      being returned for that value in the output tensor. (Note that, because
-      the value is -1 and not 0, this will allow you to effectively drop OOV
-      values from categorical encodings.)
+      1. If this value is more than 1, OOV inputs are modulated to determine
+      their OOV value; if this value is 0, passing an OOV input will result in
+      a '-1' being returned for that value in the output tensor. (Note that,
+      because the value is -1 and not 0, this will allow you to effectively drop
+      OOV values from categorical encodings.)
     mask_value: A value that represents masked inputs, and which is mapped to
       index 0. Defaults to 0. If set to None, no mask term will be added and the
-      OOV tokens, if any, will be indexed from (0...num_oov_tokens) instead of
-      (1...num_oov_tokens+1).
+      OOV values, if any, will be indexed from (0...num_oov_values) instead of
+      (1...num_oov_values+1).
     oov_value: The value representing an out-of-vocabulary value. Defaults to
       -1.
     vocabulary: An optional list of values, or a path to a text file containing
@@ -87,7 +89,7 @@ class IntegerLookup(index_lookup.IndexLookup):
   [0, -1, 42, 1138, 1000, 36, 12]
 
   Note how the mask value 0 and the OOV value -1 have been added to the
-  vocabulary. The remaining tokens are sorted by frequency (1138, which has
+  vocabulary. The remaining values are sorted by frequency (1138, which has
   2 occurrences, is first) then by inverse sort order.
 
   >>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]])
@@ -99,6 +101,27 @@ class IntegerLookup(index_lookup.IndexLookup):
          [2, 4, 5]])>
 
 
+  Lookups with multiple OOV tokens.
+
+  This example demonstrates how to use a lookup layer with multiple OOV tokens.
+  When a layer is created with more than one OOV token, any OOV values are
+  hashed into the number of OOV buckets, distributing OOV values in a
+  deterministic fashion across the set.
+
+  >>> vocab = [12, 36, 1138, 42]
+  >>> data = tf.constant([[12, 1138, 42], [37, 1000, 36]])
+  >>> layer = IntegerLookup(vocabulary=vocab, num_oov_indices=2)
+  >>> layer(data)
+  <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
+  array([[3, 5, 6],
+         [2, 1, 4]])>
+
+  Note that the output for OOV value 37 is 2, while the output for OOV value
+  1000 is 1. The in-vocab terms have their output index increased by 1 from
+  earlier examples (12 maps to 3, etc) in order to make space for the extra OOV
+  value.
+
+
   Inverse lookup
 
   This example demonstrates how to map indices to values using this layer. (You

tensorflow/python/keras/layers/preprocessing/integer_lookup_v1.py

@@ -19,7 +19,9 @@ from __future__ import print_function
 
 from tensorflow.python.keras.layers.preprocessing import index_lookup_v1
 from tensorflow.python.keras.layers.preprocessing import integer_lookup
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export(v1=["keras.layers.experimental.preprocessing.IntegerLookup"])
 class IntegerLookup(integer_lookup.IntegerLookup, index_lookup_v1.IndexLookup):
   pass

tensorflow/python/keras/layers/preprocessing/string_lookup.py

@@ -20,8 +20,10 @@ from __future__ import print_function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.keras.layers.preprocessing import index_lookup
 from tensorflow.python.keras.layers.preprocessing import table_utils
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export("keras.layers.experimental.preprocessing.StringLookup", v1=[])
 class StringLookup(index_lookup.IndexLookup):
   """Maps strings from a vocabulary to integer indices.
 
@@ -52,7 +54,7 @@ class StringLookup(index_lookup.IndexLookup):
       will be added and the OOV tokens, if any, will be indexed from
       (0...num_oov_indices) instead of (1...num_oov_indices+1).
     oov_token: The token representing an out-of-vocabulary value. Defaults to
-      "[OOV]".
+      "[UNK]".
     vocabulary: An optional list of vocabulary terms, or a path to a text file
       containing a vocabulary to load into this layer. The file should contain
       one token per line. If the list or file contains the same token multiple
@@ -85,9 +87,9 @@ class StringLookup(index_lookup.IndexLookup):
   >>> layer = StringLookup()
   >>> layer.adapt(data)
   >>> layer.get_vocabulary()
-  ['', '[OOV]', 'd', 'z', 'c', 'b', 'a']
+  ['', '[UNK]', 'd', 'z', 'c', 'b', 'a']
 
-  Note how the mask token '' and the OOV token [OOV] have been added to the
+  Note how the mask token '' and the OOV token [UNK] have been added to the
   vocabulary. The remaining tokens are sorted by frequency ('d', which has
   2 occurrences, is first) then by inverse sort order.
 
@@ -99,6 +101,25 @@ class StringLookup(index_lookup.IndexLookup):
   array([[6, 4, 2],
          [2, 3, 5]])>
 
+  Lookups with multiple OOV tokens.
+
+  This example demonstrates how to use a lookup layer with multiple OOV tokens.
+  When a layer is created with more than one OOV token, any OOV values are
+  hashed into the number of OOV buckets, distributing OOV values in a
+  deterministic fashion across the set.
+
+  >>> vocab = ["a", "b", "c", "d"]
+  >>> data = tf.constant([["a", "c", "d"], ["m", "z", "b"]])
+  >>> layer = StringLookup(vocabulary=vocab, num_oov_indices=2)
+  >>> layer(data)
+  <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
+  array([[3, 5, 6],
+         [1, 2, 4]])>
+
+  Note that the output for OOV value 'm' is 1, while the output for OOV value
+  'z' is 2. The in-vocab terms have their output index increased by 1 from
+  earlier examples (a maps to 3, etc) in order to make space for the extra OOV
+  value.
 
   Inverse lookup
 
@@ -112,7 +133,7 @@ class StringLookup(index_lookup.IndexLookup):
   >>> layer(data)
   <tf.Tensor: shape=(2, 3), dtype=string, numpy=
   array([[b'a', b'c', b'd'],
-         [b'd', b'[OOV]', b'b']], dtype=object)>
+         [b'd', b'[UNK]', b'b']], dtype=object)>
 
   Note that the integer 5, which is out of the vocabulary space, returns an OOV
   token.
@@ -131,9 +152,9 @@ class StringLookup(index_lookup.IndexLookup):
   >>> i_layer(int_data)
   <tf.Tensor: shape=(2, 3), dtype=string, numpy=
   array([[b'a', b'c', b'd'],
-         [b'd', b'[OOV]', b'b']], dtype=object)>
+         [b'd', b'[UNK]', b'b']], dtype=object)>
 
-  In this example, the input value 'z' resulted in an output of '[OOV]', since
+  In this example, the input value 'z' resulted in an output of '[UNK]', since
   1000 was not in the vocabulary - it got represented as an OOV, and all OOV
   values are returned as '[OOV}' in the inverse layer. Also, note that for the
   inverse to work, you must have already set the forward layer vocabulary
@@ -144,9 +165,9 @@ class StringLookup(index_lookup.IndexLookup):
                max_tokens=None,
                num_oov_indices=1,
                mask_token="",
-               oov_token="[OOV]",
+               oov_token="[UNK]",
                vocabulary=None,
-               encoding="utf-8",
+               encoding=None,
                invert=False,
                **kwargs):
     allowed_dtypes = [dtypes.string]
@@ -158,6 +179,9 @@ class StringLookup(index_lookup.IndexLookup):
     if "dtype" not in kwargs:
       kwargs["dtype"] = dtypes.string
 
+    if encoding is None:
+      encoding = "utf-8"
+
     if vocabulary is not None:
       if isinstance(vocabulary, str):
         vocabulary = table_utils.get_vocabulary_from_file(vocabulary, encoding)

tensorflow/python/keras/layers/preprocessing/string_lookup_test.py

@@ -36,6 +36,7 @@ from tensorflow.python.keras.layers.preprocessing import string_lookup
 from tensorflow.python.keras.layers.preprocessing import string_lookup_v1
 from tensorflow.python.keras.saving import save
 from tensorflow.python.keras.utils.generic_utils import CustomObjectScope
+from tensorflow.python.ops.ragged import ragged_factory_ops
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 
@@ -155,7 +156,7 @@ class StringLookupVocabularyTest(keras_parameterized.TestCase,
 
   def test_get_vocab_returns_str(self):
     vocab_data = ["earth", "wind", "and", "fire"]
-    expected_vocab = ["", "[OOV]", "earth", "wind", "and", "fire"]
+    expected_vocab = ["", "[UNK]", "earth", "wind", "and", "fire"]
     layer = get_layer_class()(vocabulary=vocab_data)
     layer_vocab = layer.get_vocabulary()
     self.assertAllEqual(expected_vocab, layer_vocab)
@@ -205,7 +206,7 @@ class StringLookupVocabularyTest(keras_parameterized.TestCase,
     input_array = np.array([["earth", "wind", "and", "fire"],
                             ["fire", "and", "earth", "michigan"]])
     expected_output = np.array([["earth", "wind", "and", "fire"],
-                                ["fire", "and", "earth", "[OOV]"]])
+                                ["fire", "and", "earth", "[UNK]"]])
 
     input_data = keras.Input(shape=(None,), dtype=dtypes.string)
     layer = get_layer_class()(vocabulary=vocab_data)
@@ -217,6 +218,21 @@ class StringLookupVocabularyTest(keras_parameterized.TestCase,
     output_dataset = model.predict(input_array)
     self.assertAllEqual(expected_output, output_dataset)
 
+  def test_ragged_string_input_multi_bucket(self):
+    vocab_data = ["earth", "wind", "and", "fire"]
+    input_array = ragged_factory_ops.constant([["earth", "wind", "fire"],
+                                               ["fire", "and", "earth",
+                                                "ohio"]])
+    expected_output = [[3, 4, 6], [6, 5, 3, 2]]
+
+    input_data = keras.Input(shape=(None,), dtype=dtypes.string, ragged=True)
+    layer = get_layer_class()(num_oov_indices=2)
+    layer.set_vocabulary(vocab_data)
+    int_data = layer(input_data)
+    model = keras.Model(inputs=input_data, outputs=int_data)
+    output_dataset = model.predict(input_array)
+    self.assertAllEqual(expected_output, output_dataset)
+
 
 @keras_parameterized.run_all_keras_modes(always_skip_eager=True)
 class StringLookupSaveableTest(keras_parameterized.TestCase,

tensorflow/python/keras/layers/preprocessing/string_lookup_v1.py

@@ -19,7 +19,9 @@ from __future__ import print_function
 
 from tensorflow.python.keras.layers.preprocessing import index_lookup_v1
 from tensorflow.python.keras.layers.preprocessing import string_lookup
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export(v1=["keras.layers.experimental.preprocessing.StringLookup"])
 class StringLookup(string_lookup.StringLookup, index_lookup_v1.IndexLookup):
   pass

tensorflow/python/keras/layers/serialization.py

@@ -51,8 +51,12 @@ from tensorflow.python.keras.layers.preprocessing import category_encoding_v1
 from tensorflow.python.keras.layers.preprocessing import discretization
 from tensorflow.python.keras.layers.preprocessing import hashing
 from tensorflow.python.keras.layers.preprocessing import image_preprocessing
+from tensorflow.python.keras.layers.preprocessing import integer_lookup as preprocessing_integer_lookup
+from tensorflow.python.keras.layers.preprocessing import integer_lookup_v1 as preprocessing_integer_lookup_v1
 from tensorflow.python.keras.layers.preprocessing import normalization as preprocessing_normalization
 from tensorflow.python.keras.layers.preprocessing import normalization_v1 as preprocessing_normalization_v1
+from tensorflow.python.keras.layers.preprocessing import string_lookup as preprocessing_string_lookup
+from tensorflow.python.keras.layers.preprocessing import string_lookup_v1 as preprocessing_string_lookup_v1
 from tensorflow.python.keras.layers.preprocessing import text_vectorization as preprocessing_text_vectorization
 from tensorflow.python.keras.layers.preprocessing import text_vectorization_v1 as preprocessing_text_vectorization_v1
 from tensorflow.python.keras.utils import generic_utils
@@ -63,11 +67,13 @@ from tensorflow.python.util.tf_export import keras_export
 ALL_MODULES = (base_layer, input_layer, advanced_activations, convolutional,
                convolutional_recurrent, core, cudnn_recurrent, dense_attention,
                embeddings, einsum_dense, local, merge, noise, normalization,
-               pooling, image_preprocessing, preprocessing_normalization_v1,
+               pooling, image_preprocessing, preprocessing_integer_lookup_v1,
+               preprocessing_normalization_v1, preprocessing_string_lookup_v1,
                preprocessing_text_vectorization_v1, recurrent, wrappers,
                hashing, category_crossing, category_encoding_v1, discretization)
 ALL_V2_MODULES = (rnn_cell_wrapper_v2, normalization_v2, recurrent_v2,
-                  preprocessing_normalization, preprocessing_text_vectorization,
+                  preprocessing_integer_lookup, preprocessing_normalization,
+                  preprocessing_string_lookup, preprocessing_text_vectorization,
                   category_encoding)
 # ALL_OBJECTS is meant to be a global mutable. Hence we need to make it
 # thread-local to avoid concurrent mutations.

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-integer-lookup.__metaclass__.pbtxt

@@ -0,0 +1,14 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.IntegerLookup.__metaclass__"
+tf_class {
+  is_instance: "<type \'type\'>"
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "mro"
+  }
+  member_method {
+    name: "register"
+    argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-integer-lookup.pbtxt

@@ -0,0 +1,240 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.IntegerLookup"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.integer_lookup_v1.IntegerLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.integer_lookup.IntegerLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup_v1.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer_v1.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.PreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'max_values\', \'num_oov_indices\', \'mask_value\', \'oov_value\', \'vocabulary\', \'invert\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'0\', \'-1\', \'None\', \'False\'], "
+  }
+  member_method {
+    name: "adapt"
+    argspec: "args=[\'self\', \'data\', \'reset_state\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_vocabulary"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_vocabulary"
+    argspec: "args=[\'self\', \'vocab\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "vocab_size"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-string-lookup.__metaclass__.pbtxt

@@ -0,0 +1,14 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.StringLookup.__metaclass__"
+tf_class {
+  is_instance: "<type \'type\'>"
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "mro"
+  }
+  member_method {
+    name: "register"
+    argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-string-lookup.pbtxt

@@ -0,0 +1,240 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.StringLookup"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.string_lookup_v1.StringLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.string_lookup.StringLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup_v1.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer_v1.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.PreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'max_tokens\', \'num_oov_indices\', \'mask_token\', \'oov_token\', \'vocabulary\', \'encoding\', \'invert\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'\', \'[UNK]\', \'None\', \'None\', \'False\'], "
+  }
+  member_method {
+    name: "adapt"
+    argspec: "args=[\'self\', \'data\', \'reset_state\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_vocabulary"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_vocabulary"
+    argspec: "args=[\'self\', \'vocab\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "vocab_size"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.pbtxt

@@ -20,6 +20,10 @@ tf_module {
     name: "Hashing"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "IntegerLookup"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Normalization"
     mtype: "<type \'type\'>"
@@ -68,6 +72,10 @@ tf_module {
     name: "Resizing"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "StringLookup"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "TextVectorization"
     mtype: "<type \'type\'>"

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-integer-lookup.__metaclass__.pbtxt

@@ -0,0 +1,14 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.IntegerLookup.__metaclass__"
+tf_class {
+  is_instance: "<type \'type\'>"
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "mro"
+  }
+  member_method {
+    name: "register"
+    argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-integer-lookup.pbtxt

@@ -0,0 +1,237 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.IntegerLookup"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.integer_lookup.IntegerLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.PreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'max_values\', \'num_oov_indices\', \'mask_value\', \'oov_value\', \'vocabulary\', \'invert\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'0\', \'-1\', \'None\', \'False\'], "
+  }
+  member_method {
+    name: "adapt"
+    argspec: "args=[\'self\', \'data\', \'reset_state\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_vocabulary"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_vocabulary"
+    argspec: "args=[\'self\', \'vocab\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "vocab_size"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-string-lookup.__metaclass__.pbtxt

@@ -0,0 +1,14 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.StringLookup.__metaclass__"
+tf_class {
+  is_instance: "<type \'type\'>"
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "mro"
+  }
+  member_method {
+    name: "register"
+    argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-string-lookup.pbtxt

@@ -0,0 +1,237 @@
+path: "tensorflow.keras.layers.experimental.preprocessing.StringLookup"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.string_lookup.StringLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.index_lookup.IndexLookup\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.CombinerPreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_preprocessing_layer.PreprocessingLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'max_tokens\', \'num_oov_indices\', \'mask_token\', \'oov_token\', \'vocabulary\', \'encoding\', \'invert\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'\', \'[UNK]\', \'None\', \'None\', \'False\'], "
+  }
+  member_method {
+    name: "adapt"
+    argspec: "args=[\'self\', \'data\', \'reset_state\'], varargs=None, keywords=None, defaults=[\'True\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_vocabulary"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_vocabulary"
+    argspec: "args=[\'self\', \'vocab\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "vocab_size"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.pbtxt

@@ -20,6 +20,10 @@ tf_module {
     name: "Hashing"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "IntegerLookup"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Normalization"
     mtype: "<type \'type\'>"
@@ -68,6 +72,10 @@ tf_module {
     name: "Resizing"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "StringLookup"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "TextVectorization"
     mtype: "<type \'type\'>"