prompt: 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()
  
  ```
  
golden_diff: 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,
  
verification_info: {"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"}]}