We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Simplify docstring example of `TokenAndPositionEmbedding`
The current [docstring example](https://github.com/keras-team/keras-nlp/blob/3a951a9f5442b77a0c474bb8b61406f65c7ef924/keras_nlp/layers/token_and_position_embedding.py#L45) of `TokenAndPositionEmbedding` is a bit more complicated than needed and should better match newer examples like those in [`keras_nlp.models.BertBackbone`](https://github.com/keras-team/keras-nlp/blob/3a951a9f5442b77a0c474bb8b61406f65c7ef924/keras_nlp/models/bert/bert_backbone.py#L67).

Make the following changes to the docstring example:
* Create a real input of the correct shape using `tf.ones`
* Inline constant values like `"seq_length"` directly into the function calls

Be sure to check that your example runs on colab or your local machine!

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `keras_nlp/layers/token_and_position_embedding.py`
Content:
```
1 # Copyright 2023 The KerasNLP Authors
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     https://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 """Creates an Embedding Layer and adds Positional Embeddings"""
16 
17 from tensorflow import keras
18 
19 import keras_nlp.layers
20 from keras_nlp.utils.keras_utils import clone_initializer
21 
22 
23 @keras.utils.register_keras_serializable(package="keras_nlp")
24 class TokenAndPositionEmbedding(keras.layers.Layer):
25     """A layer which sums a token and position embedding.
26 
27     Token and position embeddings are ways of representing words and their order
28     in a sentence. This layer creates a `keras.layers.Embedding` token embedding
29     and a `keras_nlp.layers.PositionEmbedding` position embedding and sums their
30     output when called. This layer assumes that the last dimension in the input
31     corresponds to the sequence dimension.
32 
33     Args:
34         vocabulary_size: The size of the vocabulary.
35         sequence_length: The maximum length of input sequence
36         embedding_dim: The output dimension of the embedding layer
37         embeddings_initializer: The initializer to use for the Embedding
38             Layers
39         mask_zero: Boolean, whether or not the input value 0 is a special
40             "padding" value that should be masked out.
41             This is useful when using recurrent layers which may take variable
42             length input. If this is True, then all subsequent layers in the
43             model need to support masking or an exception will be raised.
44             If mask_zero` is set to True, as a consequence, index 0 cannot be
45             used in the vocabulary
46             (input_dim should equal size of vocabulary + 1).
47 
48     Examples:
49     ```python
50     seq_length = 50
51     vocab_size = 5000
52     embed_dim = 128
53     inputs = keras.Input(shape=(seq_length,))
54     embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(
55         vocabulary_size=vocab_size,
56         sequence_length=seq_length,
57         embedding_dim=embed_dim,
58     )
59     outputs = embedding_layer(inputs)
60     ```
61     """
62 
63     def __init__(
64         self,
65         vocabulary_size,
66         sequence_length,
67         embedding_dim,
68         embeddings_initializer="glorot_uniform",
69         mask_zero=False,
70         **kwargs
71     ):
72         super().__init__(**kwargs)
73         if vocabulary_size is None:
74             raise ValueError(
75                 "`vocabulary_size` must be an Integer, received `None`."
76             )
77         if sequence_length is None:
78             raise ValueError(
79                 "`sequence_length` must be an Integer, received `None`."
80             )
81         if embedding_dim is None:
82             raise ValueError(
83                 "`embedding_dim` must be an Integer, received `None`."
84             )
85         self.vocabulary_size = int(vocabulary_size)
86         self.sequence_length = int(sequence_length)
87         self.embedding_dim = int(embedding_dim)
88         self.embeddings_initializer = keras.initializers.get(
89             embeddings_initializer
90         )
91         self.token_embedding = keras.layers.Embedding(
92             vocabulary_size,
93             embedding_dim,
94             embeddings_initializer=clone_initializer(
95                 self.embeddings_initializer
96             ),
97             mask_zero=mask_zero,
98             name="token_embedding"
99             + str(keras.backend.get_uid("token_embedding")),
100         )
101         self.position_embedding = keras_nlp.layers.PositionEmbedding(
102             sequence_length=sequence_length,
103             initializer=clone_initializer(self.embeddings_initializer),
104             name="position_embedding"
105             + str(keras.backend.get_uid("position_embedding")),
106         )
107         self.supports_masking = self.token_embedding.supports_masking
108 
109     def get_config(self):
110         config = super().get_config()
111         config.update(
112             {
113                 "vocabulary_size": self.vocabulary_size,
114                 "sequence_length": self.sequence_length,
115                 "embedding_dim": self.embedding_dim,
116                 "embeddings_initializer": keras.initializers.serialize(
117                     self.embeddings_initializer
118                 ),
119                 "mask_zero": self.token_embedding.mask_zero,
120             },
121         )
122         return config
123 
124     def call(self, inputs):
125         embedded_tokens = self.token_embedding(inputs)
126         embedded_positions = self.position_embedding(embedded_tokens)
127         outputs = embedded_tokens + embedded_positions
128         return outputs
129 
130     def compute_mask(self, inputs, mask=None):
131         return self.token_embedding.compute_mask(inputs, mask=mask)
132
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/keras_nlp/layers/token_and_position_embedding.py b/keras_nlp/layers/token_and_position_embedding.py
--- a/keras_nlp/layers/token_and_position_embedding.py
+++ b/keras_nlp/layers/token_and_position_embedding.py
@@ -47,14 +47,11 @@
 
     Examples:
     ```python
-    seq_length = 50
-    vocab_size = 5000
-    embed_dim = 128
-    inputs = keras.Input(shape=(seq_length,))
+    inputs = tf.ones(shape=(1, 50), dtype=tf.int64)
     embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(
-        vocabulary_size=vocab_size,
-        sequence_length=seq_length,
-        embedding_dim=embed_dim,
+        vocabulary_size=10_000,
+        sequence_length=50,
+        embedding_dim=128,
     )
     outputs = embedding_layer(inputs)
     ```

{"golden_diff": "diff --git a/keras_nlp/layers/token_and_position_embedding.py b/keras_nlp/layers/token_and_position_embedding.py\n--- a/keras_nlp/layers/token_and_position_embedding.py\n+++ b/keras_nlp/layers/token_and_position_embedding.py\n@@ -47,14 +47,11 @@\n \n     Examples:\n     ```python\n-    seq_length = 50\n-    vocab_size = 5000\n-    embed_dim = 128\n-    inputs = keras.Input(shape=(seq_length,))\n+    inputs = tf.ones(shape=(1, 50), dtype=tf.int64)\n     embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(\n-        vocabulary_size=vocab_size,\n-        sequence_length=seq_length,\n-        embedding_dim=embed_dim,\n+        vocabulary_size=10_000,\n+        sequence_length=50,\n+        embedding_dim=128,\n     )\n     outputs = embedding_layer(inputs)\n     ```\n", "issue": "Simplify docstring example of `TokenAndPositionEmbedding`\nThe current [docstring example](https://github.com/keras-team/keras-nlp/blob/3a951a9f5442b77a0c474bb8b61406f65c7ef924/keras_nlp/layers/token_and_position_embedding.py#L45) of `TokenAndPositionEmbedding` is a bit more complicated than needed and should better match newer examples like those in [`keras_nlp.models.BertBackbone`](https://github.com/keras-team/keras-nlp/blob/3a951a9f5442b77a0c474bb8b61406f65c7ef924/keras_nlp/models/bert/bert_backbone.py#L67).\r\n\r\nMake the following changes to the docstring example:\r\n* Create a real input of the correct shape using `tf.ones`\r\n* Inline constant values like `\"seq_length\"` directly into the function calls\r\n\r\nBe sure to check that your example runs on colab or your local machine!\n", "before_files": [{"content": "# Copyright 2023 The KerasNLP Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Creates an Embedding Layer and adds Positional Embeddings\"\"\"\n\nfrom tensorflow import keras\n\nimport keras_nlp.layers\nfrom keras_nlp.utils.keras_utils import clone_initializer\n\n\n@keras.utils.register_keras_serializable(package=\"keras_nlp\")\nclass TokenAndPositionEmbedding(keras.layers.Layer):\n    \"\"\"A layer which sums a token and position embedding.\n\n    Token and position embeddings are ways of representing words and their order\n    in a sentence. This layer creates a `keras.layers.Embedding` token embedding\n    and a `keras_nlp.layers.PositionEmbedding` position embedding and sums their\n    output when called. This layer assumes that the last dimension in the input\n    corresponds to the sequence dimension.\n\n    Args:\n        vocabulary_size: The size of the vocabulary.\n        sequence_length: The maximum length of input sequence\n        embedding_dim: The output dimension of the embedding layer\n        embeddings_initializer: The initializer to use for the Embedding\n            Layers\n        mask_zero: Boolean, whether or not the input value 0 is a special\n            \"padding\" value that should be masked out.\n            This is useful when using recurrent layers which may take variable\n            length input. If this is True, then all subsequent layers in the\n            model need to support masking or an exception will be raised.\n            If mask_zero` is set to True, as a consequence, index 0 cannot be\n            used in the vocabulary\n            (input_dim should equal size of vocabulary + 1).\n\n    Examples:\n    ```python\n    seq_length = 50\n    vocab_size = 5000\n    embed_dim = 128\n    inputs = keras.Input(shape=(seq_length,))\n    embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(\n        vocabulary_size=vocab_size,\n        sequence_length=seq_length,\n        embedding_dim=embed_dim,\n    )\n    outputs = embedding_layer(inputs)\n    ```\n    \"\"\"\n\n    def __init__(\n        self,\n        vocabulary_size,\n        sequence_length,\n        embedding_dim,\n        embeddings_initializer=\"glorot_uniform\",\n        mask_zero=False,\n        **kwargs\n    ):\n        super().__init__(**kwargs)\n        if vocabulary_size is None:\n            raise ValueError(\n                \"`vocabulary_size` must be an Integer, received `None`.\"\n            )\n        if sequence_length is None:\n            raise ValueError(\n                \"`sequence_length` must be an Integer, received `None`.\"\n            )\n        if embedding_dim is None:\n            raise ValueError(\n                \"`embedding_dim` must be an Integer, received `None`.\"\n            )\n        self.vocabulary_size = int(vocabulary_size)\n        self.sequence_length = int(sequence_length)\n        self.embedding_dim = int(embedding_dim)\n        self.embeddings_initializer = keras.initializers.get(\n            embeddings_initializer\n        )\n        self.token_embedding = keras.layers.Embedding(\n            vocabulary_size,\n            embedding_dim,\n            embeddings_initializer=clone_initializer(\n                self.embeddings_initializer\n            ),\n            mask_zero=mask_zero,\n            name=\"token_embedding\"\n            + str(keras.backend.get_uid(\"token_embedding\")),\n        )\n        self.position_embedding = keras_nlp.layers.PositionEmbedding(\n            sequence_length=sequence_length,\n            initializer=clone_initializer(self.embeddings_initializer),\n            name=\"position_embedding\"\n            + str(keras.backend.get_uid(\"position_embedding\")),\n        )\n        self.supports_masking = self.token_embedding.supports_masking\n\n    def get_config(self):\n        config = super().get_config()\n        config.update(\n            {\n                \"vocabulary_size\": self.vocabulary_size,\n                \"sequence_length\": self.sequence_length,\n                \"embedding_dim\": self.embedding_dim,\n                \"embeddings_initializer\": keras.initializers.serialize(\n                    self.embeddings_initializer\n                ),\n                \"mask_zero\": self.token_embedding.mask_zero,\n            },\n        )\n        return config\n\n    def call(self, inputs):\n        embedded_tokens = self.token_embedding(inputs)\n        embedded_positions = self.position_embedding(embedded_tokens)\n        outputs = embedded_tokens + embedded_positions\n        return outputs\n\n    def compute_mask(self, inputs, mask=None):\n        return self.token_embedding.compute_mask(inputs, mask=mask)\n", "path": "keras_nlp/layers/token_and_position_embedding.py"}], "after_files": [{"content": "# Copyright 2023 The KerasNLP Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Creates an Embedding Layer and adds Positional Embeddings\"\"\"\n\nfrom tensorflow import keras\n\nimport keras_nlp.layers\nfrom keras_nlp.utils.keras_utils import clone_initializer\n\n\n@keras.utils.register_keras_serializable(package=\"keras_nlp\")\nclass TokenAndPositionEmbedding(keras.layers.Layer):\n    \"\"\"A layer which sums a token and position embedding.\n\n    Token and position embeddings are ways of representing words and their order\n    in a sentence. This layer creates a `keras.layers.Embedding` token embedding\n    and a `keras_nlp.layers.PositionEmbedding` position embedding and sums their\n    output when called. This layer assumes that the last dimension in the input\n    corresponds to the sequence dimension.\n\n    Args:\n        vocabulary_size: The size of the vocabulary.\n        sequence_length: The maximum length of input sequence\n        embedding_dim: The output dimension of the embedding layer\n        embeddings_initializer: The initializer to use for the Embedding\n            Layers\n        mask_zero: Boolean, whether or not the input value 0 is a special\n            \"padding\" value that should be masked out.\n            This is useful when using recurrent layers which may take variable\n            length input. If this is True, then all subsequent layers in the\n            model need to support masking or an exception will be raised.\n            If mask_zero` is set to True, as a consequence, index 0 cannot be\n            used in the vocabulary\n            (input_dim should equal size of vocabulary + 1).\n\n    Examples:\n    ```python\n    inputs = tf.ones(shape=(1, 50), dtype=tf.int64)\n    embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(\n        vocabulary_size=10_000,\n        sequence_length=50,\n        embedding_dim=128,\n    )\n    outputs = embedding_layer(inputs)\n    ```\n    \"\"\"\n\n    def __init__(\n        self,\n        vocabulary_size,\n        sequence_length,\n        embedding_dim,\n        embeddings_initializer=\"glorot_uniform\",\n        mask_zero=False,\n        **kwargs\n    ):\n        super().__init__(**kwargs)\n        if vocabulary_size is None:\n            raise ValueError(\n                \"`vocabulary_size` must be an Integer, received `None`.\"\n            )\n        if sequence_length is None:\n            raise ValueError(\n                \"`sequence_length` must be an Integer, received `None`.\"\n            )\n        if embedding_dim is None:\n            raise ValueError(\n                \"`embedding_dim` must be an Integer, received `None`.\"\n            )\n        self.vocabulary_size = int(vocabulary_size)\n        self.sequence_length = int(sequence_length)\n        self.embedding_dim = int(embedding_dim)\n        self.embeddings_initializer = keras.initializers.get(\n            embeddings_initializer\n        )\n        self.token_embedding = keras.layers.Embedding(\n            vocabulary_size,\n            embedding_dim,\n            embeddings_initializer=clone_initializer(\n                self.embeddings_initializer\n            ),\n            mask_zero=mask_zero,\n            name=\"token_embedding\"\n            + str(keras.backend.get_uid(\"token_embedding\")),\n        )\n        self.position_embedding = keras_nlp.layers.PositionEmbedding(\n            sequence_length=sequence_length,\n            initializer=clone_initializer(self.embeddings_initializer),\n            name=\"position_embedding\"\n            + str(keras.backend.get_uid(\"position_embedding\")),\n        )\n        self.supports_masking = self.token_embedding.supports_masking\n\n    def get_config(self):\n        config = super().get_config()\n        config.update(\n            {\n                \"vocabulary_size\": self.vocabulary_size,\n                \"sequence_length\": self.sequence_length,\n                \"embedding_dim\": self.embedding_dim,\n                \"embeddings_initializer\": keras.initializers.serialize(\n                    self.embeddings_initializer\n                ),\n                \"mask_zero\": self.token_embedding.mask_zero,\n            },\n        )\n        return config\n\n    def call(self, inputs):\n        embedded_tokens = self.token_embedding(inputs)\n        embedded_positions = self.position_embedding(embedded_tokens)\n        outputs = embedded_tokens + embedded_positions\n        return outputs\n\n    def compute_mask(self, inputs, mask=None):\n        return self.token_embedding.compute_mask(inputs, mask=mask)\n", "path": "keras_nlp/layers/token_and_position_embedding.py"}]}