We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
kfserving-samples bucket on GCS was gone
/kind bug

**What steps did you take and what happened:**
The existing codebase is running some test code with a prebuild model file located in GCS, especially `gs://kfserving-samples/models/tensorflow/flowers` but now seems the bucket is gone already. 
Do we have other alternative buckets? or this bucket should be live forever?

```
gsutil cp -r gs://kfserving-samples/models/tensorflow/flowers flowers

BucketNotFoundException: 404 gs://kfserving-samples bucket does not exist.
```



**What did you expect to happen:**
the model file should be downloaded.


**Anything else you would like to add:**
No

**Environment:**
not related to environment.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `docs/samples/pipelines/sample-tf-pipeline.py`
Content:
```
1 #
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at
5 #
6 #    http://www.apache.org/licenses/LICENSE-2.0
7 #
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
13 
14 import kfp.compiler as compiler
15 import kfp.dsl as dsl
16 from kfp import components
17 
18 # kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'
19 #                                                  'master/components/kubeflow/kfserving/component.yaml')
20 kserve_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'
21                                                'master/components/kserve/component.yaml')
22 
23 
24 @dsl.pipeline(
25     name='KServe pipeline',
26     description='A pipeline for KServe.'
27 )
28 def kservePipeline(
29         action='apply',
30         model_name='tensorflow-sample',
31         model_uri='gs://kfserving-samples/models/tensorflow/flowers',
32         namespace='anonymous',
33         framework='tensorflow'):
34     kserve_op(action=action,
35               model_name=model_name,
36               model_uri=model_uri,
37               namespace=namespace,
38               framework=framework)
39 
40 
41 if __name__ == '__main__':
42     compiler.Compiler().compile(kservePipeline, __file__ + '.tar.gz')
43
```
--- 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/docs/samples/pipelines/sample-tf-pipeline.py b/docs/samples/pipelines/sample-tf-pipeline.py
--- a/docs/samples/pipelines/sample-tf-pipeline.py
+++ b/docs/samples/pipelines/sample-tf-pipeline.py
@@ -28,7 +28,7 @@
 def kservePipeline(
         action='apply',
         model_name='tensorflow-sample',
-        model_uri='gs://kfserving-samples/models/tensorflow/flowers',
+        model_uri='gs://kfserving-examples/models/tensorflow/flowers',
         namespace='anonymous',
         framework='tensorflow'):
     kserve_op(action=action,

{"golden_diff": "diff --git a/docs/samples/pipelines/sample-tf-pipeline.py b/docs/samples/pipelines/sample-tf-pipeline.py\n--- a/docs/samples/pipelines/sample-tf-pipeline.py\n+++ b/docs/samples/pipelines/sample-tf-pipeline.py\n@@ -28,7 +28,7 @@\n def kservePipeline(\n         action='apply',\n         model_name='tensorflow-sample',\n-        model_uri='gs://kfserving-samples/models/tensorflow/flowers',\n+        model_uri='gs://kfserving-examples/models/tensorflow/flowers',\n         namespace='anonymous',\n         framework='tensorflow'):\n     kserve_op(action=action,\n", "issue": "kfserving-samples bucket on GCS was gone\n/kind bug\r\n\r\n**What steps did you take and what happened:**\r\nThe existing codebase is running some test code with a prebuild model file located in GCS, especially `gs://kfserving-samples/models/tensorflow/flowers` but now seems the bucket is gone already. \r\nDo we have other alternative buckets? or this bucket should be live forever?\r\n\r\n```\r\ngsutil cp -r gs://kfserving-samples/models/tensorflow/flowers flowers\r\n\r\nBucketNotFoundException: 404 gs://kfserving-samples bucket does not exist.\r\n```\r\n\r\n\r\n\r\n**What did you expect to happen:**\r\nthe model file should be downloaded.\r\n\r\n\r\n**Anything else you would like to add:**\r\nNo\r\n\r\n**Environment:**\r\nnot related to environment.\r\n\n", "before_files": [{"content": "#\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#    http://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\nimport kfp.compiler as compiler\nimport kfp.dsl as dsl\nfrom kfp import components\n\n# kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'\n#                                                  'master/components/kubeflow/kfserving/component.yaml')\nkserve_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'\n                                               'master/components/kserve/component.yaml')\n\n\n@dsl.pipeline(\n    name='KServe pipeline',\n    description='A pipeline for KServe.'\n)\ndef kservePipeline(\n        action='apply',\n        model_name='tensorflow-sample',\n        model_uri='gs://kfserving-samples/models/tensorflow/flowers',\n        namespace='anonymous',\n        framework='tensorflow'):\n    kserve_op(action=action,\n              model_name=model_name,\n              model_uri=model_uri,\n              namespace=namespace,\n              framework=framework)\n\n\nif __name__ == '__main__':\n    compiler.Compiler().compile(kservePipeline, __file__ + '.tar.gz')\n", "path": "docs/samples/pipelines/sample-tf-pipeline.py"}], "after_files": [{"content": "#\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#    http://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\nimport kfp.compiler as compiler\nimport kfp.dsl as dsl\nfrom kfp import components\n\n# kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'\n#                                                  'master/components/kubeflow/kfserving/component.yaml')\nkserve_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/'\n                                               'master/components/kserve/component.yaml')\n\n\n@dsl.pipeline(\n    name='KServe pipeline',\n    description='A pipeline for KServe.'\n)\ndef kservePipeline(\n        action='apply',\n        model_name='tensorflow-sample',\n        model_uri='gs://kfserving-examples/models/tensorflow/flowers',\n        namespace='anonymous',\n        framework='tensorflow'):\n    kserve_op(action=action,\n              model_name=model_name,\n              model_uri=model_uri,\n              namespace=namespace,\n              framework=framework)\n\n\nif __name__ == '__main__':\n    compiler.Compiler().compile(kservePipeline, __file__ + '.tar.gz')\n", "path": "docs/samples/pipelines/sample-tf-pipeline.py"}]}