We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
apply #556 manually
I manually applied the patch from #556. Not sure what's up with that PR

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `01_getting_started/hello_world.py`
Content:
```
1 # # Hello, world!
2 #
3 # This is a trivial example of a Modal function, but it illustrates a few features:
4 #
5 # * You can print things to stdout and stderr.
6 # * You can return data.
7 # * You can map over a function.
8 #
9 # ## Import Modal and define the app
10 #
11 # Let's start with the top level imports.
12 # You need to import Modal and define the app.
13 # A stub is an object that defines everything that will be run.
14 
15 import sys
16 
17 import modal
18 
19 stub = modal.Stub("example-hello-world")
20 
21 # ## Defining a function
22 #
23 # Here we define a Modal function using the `modal.function` decorator.
24 # The body of the function will automatically be run remotely.
25 # This particular function is pretty silly: it just prints "hello"
26 # and "world" alternatingly to standard out and standard error.
27 
28 
29 @stub.function()
30 def f(i):
31     if i % 2 == 0:
32         print("hello", i)
33     else:
34         print("world", i, file=sys.stderr)
35 
36     return i * i
37 
38 
39 # ## Running it
40 #
41 # Finally, let's actually invoke it.
42 # We put this invocation code inside a `@stub.local_entrypoint()`.
43 # This is because this module will be imported in the cloud, and we don't want
44 # this code to be executed a second time in the cloud.
45 #
46 # Run `modal run hello_world.py` and the `@stub.local_entrypoint()` decorator will handle
47 # starting the Modal app and then executing the wrapped function body.
48 #
49 # Inside the `main()` function body, we are calling the function `f` in three ways:
50 #
51 # 1  As a simple local call, `f(1000)`
52 # 2. As a simple *remote* call `f.remote(1000)`
53 # 3. By mapping over the integers `0..19`
54 
55 
56 @stub.local_entrypoint()
57 def main():
58     # Call the function locally.
59     print(f.local(1000))
60 
61     # Call the function remotely.
62     print(f.remote(1000))
63 
64     # Parallel map.
65     total = 0
66     for ret in f.map(range(20)):
67         total += ret
68 
69     print(total)
70 
71 
72 # ## What happens?
73 #
74 # When you do `.remote` on function `f`, Modal will execute `f` **in the cloud,**
75 # not locally on your computer. It will take the code, put it inside a
76 # container, run it, and stream all the output back to your local
77 # computer.
78 #
79 # Try doing one of these things next.
80 #
81 # ### Change the code and run again
82 #
83 # For instance, change the `print` statement in the function `f`.
84 # You can see that the latest code is always run.
85 #
86 # Modal's goal is to make running code in the cloud feel like you're
87 # running code locally. You don't need to run any commands to rebuild,
88 # push containers, or go to a web UI to download logs.
89 #
90 # ### Map over a larger dataset
91 #
92 # Change the map range from 20 to some large number. You can see that
93 # Modal will create and run more containers in parallel.
94 #
95 # The function `f` is obviously silly and doesn't do much, but you could
96 # imagine something more significant, like:
97 #
98 # * Training a machine learning model
99 # * Transcoding media
100 # * Backtesting a trading algorithm.
101 #
102 # Modal lets you parallelize that operation trivially by running hundreds or
103 # thousands of containers in the cloud.
104
```
--- 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/01_getting_started/hello_world.py b/01_getting_started/hello_world.py
--- a/01_getting_started/hello_world.py
+++ b/01_getting_started/hello_world.py
@@ -48,7 +48,7 @@
 #
 # Inside the `main()` function body, we are calling the function `f` in three ways:
 #
-# 1  As a simple local call, `f(1000)`
+# 1  As a simple local call, `f.local(1000)`
 # 2. As a simple *remote* call `f.remote(1000)`
 # 3. By mapping over the integers `0..19`

{"golden_diff": "diff --git a/01_getting_started/hello_world.py b/01_getting_started/hello_world.py\n--- a/01_getting_started/hello_world.py\n+++ b/01_getting_started/hello_world.py\n@@ -48,7 +48,7 @@\n #\n # Inside the `main()` function body, we are calling the function `f` in three ways:\n #\n-# 1  As a simple local call, `f(1000)`\n+# 1  As a simple local call, `f.local(1000)`\n # 2. As a simple *remote* call `f.remote(1000)`\n # 3. By mapping over the integers `0..19`\n", "issue": "apply #556 manually\nI manually applied the patch from #556. Not sure what's up with that PR\n", "before_files": [{"content": "# # Hello, world!\n#\n# This is a trivial example of a Modal function, but it illustrates a few features:\n#\n# * You can print things to stdout and stderr.\n# * You can return data.\n# * You can map over a function.\n#\n# ## Import Modal and define the app\n#\n# Let's start with the top level imports.\n# You need to import Modal and define the app.\n# A stub is an object that defines everything that will be run.\n\nimport sys\n\nimport modal\n\nstub = modal.Stub(\"example-hello-world\")\n\n# ## Defining a function\n#\n# Here we define a Modal function using the `modal.function` decorator.\n# The body of the function will automatically be run remotely.\n# This particular function is pretty silly: it just prints \"hello\"\n# and \"world\" alternatingly to standard out and standard error.\n\n\n@stub.function()\ndef f(i):\n    if i % 2 == 0:\n        print(\"hello\", i)\n    else:\n        print(\"world\", i, file=sys.stderr)\n\n    return i * i\n\n\n# ## Running it\n#\n# Finally, let's actually invoke it.\n# We put this invocation code inside a `@stub.local_entrypoint()`.\n# This is because this module will be imported in the cloud, and we don't want\n# this code to be executed a second time in the cloud.\n#\n# Run `modal run hello_world.py` and the `@stub.local_entrypoint()` decorator will handle\n# starting the Modal app and then executing the wrapped function body.\n#\n# Inside the `main()` function body, we are calling the function `f` in three ways:\n#\n# 1  As a simple local call, `f(1000)`\n# 2. As a simple *remote* call `f.remote(1000)`\n# 3. By mapping over the integers `0..19`\n\n\n@stub.local_entrypoint()\ndef main():\n    # Call the function locally.\n    print(f.local(1000))\n\n    # Call the function remotely.\n    print(f.remote(1000))\n\n    # Parallel map.\n    total = 0\n    for ret in f.map(range(20)):\n        total += ret\n\n    print(total)\n\n\n# ## What happens?\n#\n# When you do `.remote` on function `f`, Modal will execute `f` **in the cloud,**\n# not locally on your computer. It will take the code, put it inside a\n# container, run it, and stream all the output back to your local\n# computer.\n#\n# Try doing one of these things next.\n#\n# ### Change the code and run again\n#\n# For instance, change the `print` statement in the function `f`.\n# You can see that the latest code is always run.\n#\n# Modal's goal is to make running code in the cloud feel like you're\n# running code locally. You don't need to run any commands to rebuild,\n# push containers, or go to a web UI to download logs.\n#\n# ### Map over a larger dataset\n#\n# Change the map range from 20 to some large number. You can see that\n# Modal will create and run more containers in parallel.\n#\n# The function `f` is obviously silly and doesn't do much, but you could\n# imagine something more significant, like:\n#\n# * Training a machine learning model\n# * Transcoding media\n# * Backtesting a trading algorithm.\n#\n# Modal lets you parallelize that operation trivially by running hundreds or\n# thousands of containers in the cloud.\n", "path": "01_getting_started/hello_world.py"}], "after_files": [{"content": "# # Hello, world!\n#\n# This is a trivial example of a Modal function, but it illustrates a few features:\n#\n# * You can print things to stdout and stderr.\n# * You can return data.\n# * You can map over a function.\n#\n# ## Import Modal and define the app\n#\n# Let's start with the top level imports.\n# You need to import Modal and define the app.\n# A stub is an object that defines everything that will be run.\n\nimport sys\n\nimport modal\n\nstub = modal.Stub(\"example-hello-world\")\n\n# ## Defining a function\n#\n# Here we define a Modal function using the `modal.function` decorator.\n# The body of the function will automatically be run remotely.\n# This particular function is pretty silly: it just prints \"hello\"\n# and \"world\" alternatingly to standard out and standard error.\n\n\n@stub.function()\ndef f(i):\n    if i % 2 == 0:\n        print(\"hello\", i)\n    else:\n        print(\"world\", i, file=sys.stderr)\n\n    return i * i\n\n\n# ## Running it\n#\n# Finally, let's actually invoke it.\n# We put this invocation code inside a `@stub.local_entrypoint()`.\n# This is because this module will be imported in the cloud, and we don't want\n# this code to be executed a second time in the cloud.\n#\n# Run `modal run hello_world.py` and the `@stub.local_entrypoint()` decorator will handle\n# starting the Modal app and then executing the wrapped function body.\n#\n# Inside the `main()` function body, we are calling the function `f` in three ways:\n#\n# 1  As a simple local call, `f.local(1000)`\n# 2. As a simple *remote* call `f.remote(1000)`\n# 3. By mapping over the integers `0..19`\n\n\n@stub.local_entrypoint()\ndef main():\n    # Call the function locally.\n    print(f.local(1000))\n\n    # Call the function remotely.\n    print(f.remote(1000))\n\n    # Parallel map.\n    total = 0\n    for ret in f.map(range(20)):\n        total += ret\n\n    print(total)\n\n\n# ## What happens?\n#\n# When you do `.remote` on function `f`, Modal will execute `f` **in the cloud,**\n# not locally on your computer. It will take the code, put it inside a\n# container, run it, and stream all the output back to your local\n# computer.\n#\n# Try doing one of these things next.\n#\n# ### Change the code and run again\n#\n# For instance, change the `print` statement in the function `f`.\n# You can see that the latest code is always run.\n#\n# Modal's goal is to make running code in the cloud feel like you're\n# running code locally. You don't need to run any commands to rebuild,\n# push containers, or go to a web UI to download logs.\n#\n# ### Map over a larger dataset\n#\n# Change the map range from 20 to some large number. You can see that\n# Modal will create and run more containers in parallel.\n#\n# The function `f` is obviously silly and doesn't do much, but you could\n# imagine something more significant, like:\n#\n# * Training a machine learning model\n# * Transcoding media\n# * Backtesting a trading algorithm.\n#\n# Modal lets you parallelize that operation trivially by running hundreds or\n# thousands of containers in the cloud.\n", "path": "01_getting_started/hello_world.py"}]}