prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  apply #556 manually
  I manually applied the patch from #556. Not sure what's up with that PR
  
  </issue>
  <code>
  [start of 01_getting_started/hello_world.py]
  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 of 01_getting_started/hello_world.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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`
  
verification_info: {"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"}]}