We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[CI] Test examples in CI
### Anything you want to discuss about vllm.

Current scripts in `examples/` directory are not tested in CI. We should run them to ensure passing 

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/llava_example.py`
Content:
```
1 import argparse
2 import os
3 import subprocess
4 
5 import torch
6 
7 from vllm import LLM
8 from vllm.sequence import MultiModalData
9 
10 # The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
11 
12 
13 def run_llava_pixel_values():
14     llm = LLM(
15         model="llava-hf/llava-1.5-7b-hf",
16         image_input_type="pixel_values",
17         image_token_id=32000,
18         image_input_shape="1,3,336,336",
19         image_feature_size=576,
20     )
21 
22     prompt = "<image>" * 576 + (
23         "\nUSER: What is the content of this image?\nASSISTANT:")
24 
25     # This should be provided by another online or offline component.
26     images = torch.load("images/stop_sign_pixel_values.pt")
27 
28     outputs = llm.generate(prompt,
29                            multi_modal_data=MultiModalData(
30                                type=MultiModalData.Type.IMAGE, data=images))
31     for o in outputs:
32         generated_text = o.outputs[0].text
33         print(generated_text)
34 
35 
36 def run_llava_image_features():
37     llm = LLM(
38         model="llava-hf/llava-1.5-7b-hf",
39         image_input_type="image_features",
40         image_token_id=32000,
41         image_input_shape="1,576,1024",
42         image_feature_size=576,
43     )
44 
45     prompt = "<image>" * 576 + (
46         "\nUSER: What is the content of this image?\nASSISTANT:")
47 
48     # This should be provided by another online or offline component.
49     images = torch.load("images/stop_sign_image_features.pt")
50 
51     outputs = llm.generate(prompt,
52                            multi_modal_data=MultiModalData(
53                                type=MultiModalData.Type.IMAGE, data=images))
54     for o in outputs:
55         generated_text = o.outputs[0].text
56         print(generated_text)
57 
58 
59 def main(args):
60     if args.type == "pixel_values":
61         run_llava_pixel_values()
62     else:
63         run_llava_image_features()
64 
65 
66 if __name__ == "__main__":
67     parser = argparse.ArgumentParser(description="Demo on Llava")
68     parser.add_argument("--type",
69                         type=str,
70                         choices=["pixel_values", "image_features"],
71                         default="pixel_values",
72                         help="image input type")
73     args = parser.parse_args()
74     # Download from s3
75     s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
76     local_directory = "images"
77 
78     # Make sure the local directory exists or create it
79     os.makedirs(local_directory, exist_ok=True)
80 
81     # Use AWS CLI to sync the directory
82     subprocess.check_call(
83         ["aws", "s3", "sync", s3_bucket_path, local_directory])
84     main(args)
85
```
--- 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/examples/llava_example.py b/examples/llava_example.py
--- a/examples/llava_example.py
+++ b/examples/llava_example.py
@@ -78,7 +78,13 @@
     # Make sure the local directory exists or create it
     os.makedirs(local_directory, exist_ok=True)
 
-    # Use AWS CLI to sync the directory
-    subprocess.check_call(
-        ["aws", "s3", "sync", s3_bucket_path, local_directory])
+    # Use AWS CLI to sync the directory, assume anonymous access
+    subprocess.check_call([
+        "aws",
+        "s3",
+        "sync",
+        s3_bucket_path,
+        local_directory,
+        "--no-sign-request",
+    ])
     main(args)

{"golden_diff": "diff --git a/examples/llava_example.py b/examples/llava_example.py\n--- a/examples/llava_example.py\n+++ b/examples/llava_example.py\n@@ -78,7 +78,13 @@\n     # Make sure the local directory exists or create it\n     os.makedirs(local_directory, exist_ok=True)\n \n-    # Use AWS CLI to sync the directory\n-    subprocess.check_call(\n-        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n+    # Use AWS CLI to sync the directory, assume anonymous access\n+    subprocess.check_call([\n+        \"aws\",\n+        \"s3\",\n+        \"sync\",\n+        s3_bucket_path,\n+        local_directory,\n+        \"--no-sign-request\",\n+    ])\n     main(args)\n", "issue": "[CI] Test examples in CI\n### Anything you want to discuss about vllm.\n\nCurrent scripts in `examples/` directory are not tested in CI. We should run them to ensure passing \n", "before_files": [{"content": "import argparse\nimport os\nimport subprocess\n\nimport torch\n\nfrom vllm import LLM\nfrom vllm.sequence import MultiModalData\n\n# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.\n\n\ndef run_llava_pixel_values():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"pixel_values\",\n        image_token_id=32000,\n        image_input_shape=\"1,3,336,336\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_pixel_values.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef run_llava_image_features():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"image_features\",\n        image_token_id=32000,\n        image_input_shape=\"1,576,1024\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_image_features.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef main(args):\n    if args.type == \"pixel_values\":\n        run_llava_pixel_values()\n    else:\n        run_llava_image_features()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Demo on Llava\")\n    parser.add_argument(\"--type\",\n                        type=str,\n                        choices=[\"pixel_values\", \"image_features\"],\n                        default=\"pixel_values\",\n                        help=\"image input type\")\n    args = parser.parse_args()\n    # Download from s3\n    s3_bucket_path = \"s3://air-example-data-2/vllm_opensource_llava/\"\n    local_directory = \"images\"\n\n    # Make sure the local directory exists or create it\n    os.makedirs(local_directory, exist_ok=True)\n\n    # Use AWS CLI to sync the directory\n    subprocess.check_call(\n        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n    main(args)\n", "path": "examples/llava_example.py"}], "after_files": [{"content": "import argparse\nimport os\nimport subprocess\n\nimport torch\n\nfrom vllm import LLM\nfrom vllm.sequence import MultiModalData\n\n# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.\n\n\ndef run_llava_pixel_values():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"pixel_values\",\n        image_token_id=32000,\n        image_input_shape=\"1,3,336,336\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_pixel_values.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef run_llava_image_features():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"image_features\",\n        image_token_id=32000,\n        image_input_shape=\"1,576,1024\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_image_features.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef main(args):\n    if args.type == \"pixel_values\":\n        run_llava_pixel_values()\n    else:\n        run_llava_image_features()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Demo on Llava\")\n    parser.add_argument(\"--type\",\n                        type=str,\n                        choices=[\"pixel_values\", \"image_features\"],\n                        default=\"pixel_values\",\n                        help=\"image input type\")\n    args = parser.parse_args()\n    # Download from s3\n    s3_bucket_path = \"s3://air-example-data-2/vllm_opensource_llava/\"\n    local_directory = \"images\"\n\n    # Make sure the local directory exists or create it\n    os.makedirs(local_directory, exist_ok=True)\n\n    # Use AWS CLI to sync the directory, assume anonymous access\n    subprocess.check_call([\n        \"aws\",\n        \"s3\",\n        \"sync\",\n        s3_bucket_path,\n        local_directory,\n        \"--no-sign-request\",\n    ])\n    main(args)\n", "path": "examples/llava_example.py"}]}