Indentation error in Python example in "Step 4: Create a Run" of Quickstart Documentation

[Programmer-RD-AI]

Confirm this is an issue with the Python library and not an underlying OpenAI API

• This is an issue with the Python library

Describe the bug

The Python example provided in the "Step 4: Create a Run" section of the [Quickstart documentation](https://platform.openai.com/docs/assistants/quickstart#step-4-create-a-run) contains incorrect indentation, which leads to a IndentationError when executed as-is.

Specifically, the methods within the EventHandler class are not properly indented under the class definition. This may confuse users and lead to runtime errors.

To Reproduce

1. Visit the [Quickstart documentation, Step 4](https://platform.openai.com/docs/assistants/quickstart#step-4-create-a-run).
2. Copy the provided Python code snippet.
3. Attempt to execute the code in a Python environment.
4. Observe the IndentationError caused by misaligned methods in the EventHandler class.

Code snippets

# Here is the problematic snippet:  


class EventHandler(AssistantEventHandler):    
@override
def on_text_created(self, text) -> None:
  print(f"\nassistant > ", end="", flush=True)
    
@override
def on_text_delta(self, delta, snapshot):
  print(delta.value, end="", flush=True)
    
def on_tool_call_created(self, tool_call):
  print(f"\nassistant > {tool_call.type}\n", flush=True)

def on_tool_call_delta(self, delta, snapshot):
  if delta.type == 'code_interpreter':
    if delta.code_interpreter.input:
      print(delta.code_interpreter.input, end="", flush=True)
    if delta.code_interpreter.outputs:
      print(f"\n\noutput >", flush=True)
      for output in delta.code_interpreter.outputs:
        if output.type == "logs":
          print(f"\n{output.logs}", flush=True)


# Correctly indented version:  


class EventHandler(AssistantEventHandler):    
    @override
    def on_text_created(self, text) -> None:
        print(f"\nassistant > ", end="", flush=True)
    
    @override
    def on_text_delta(self, delta, snapshot):
        print(delta.value, end="", flush=True)
    
    def on_tool_call_created(self, tool_call):
        print(f"\nassistant > {tool_call.type}\n", flush=True)

    def on_tool_call_delta(self, delta, snapshot):
        if delta.type == 'code_interpreter':
            if delta.code_interpreter.input:
                print(delta.code_interpreter.input, end="", flush=True)
            if delta.code_interpreter.outputs:
                print(f"\n\noutput >", flush=True)
                for output in delta.code_interpreter.outputs:
                    if output.type == "logs":
                        print(f"\n{output.logs}", flush=True)

OS

Python version

Library version