Sweep: Create a test.py file that prints hello world

[mehulgupta2016154]

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Checklist

• test.py ✅ Commit 634e7ba
• test.py ✅ Sandbox ran successfully

[sweep-ai]

Here's the PR! #12.

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Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I looked at (click to expand). If some file is missing from here, you can mention the path in the ticket description.

Traffic_Turbo/README.md

Lines 1 to 38 in 25c2099

	# Traffic Turbo 🏍️
	Traffic Turbo is a road based environment where the agent (top left corner) is trained to reach his home (bottom right corner). The training & Testing for one of the random environments can be seen [here](https://www.youtube.com/watch?v=TfjtjKFSpmE)
	## The enviroment
	
	The environment consists of the following elements
	- Road : Reward =-3
	- Boost : Reward = 0
	- Traffic Signal : Reward =-20
	- Car Jam : Reward =-50
	- House: Reward = 500
	The end goal of the agent is to take up an optimal path so as to keep a high reward at the end of the episode. Any move is considered invalid if
	1. Goes out of the enviroment
	2. Any cell is already visited in a particular episode
	## Setting up the environment
	This has been done using Pygame library that provides GUI components & animation capabilities for python projects.
	## Training & Testing
	The agent has been trained using Q Learning technique in Reinforcement learning for ~2.k episodes using random states as initialization point for each episode.
	## Pretrained environments
	For playing around, weights for 2 environments have been trained till 2k episodes & stored in env_weights function. For trying, initialize the game_env object with '1' or 'final_v'
	Read more on [Medium](https://medium.com/data-science-in-your-pocket/game-development-using-pygame-reinforcement-learning-with-example-f5b78c768610)

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Step 2: ⌨️ Coding

• test.py ✅ Commit 634e7ba

Create test.py with contents:
• Add a Python shebang at the top of the file to specify the Python interpreter to be used for executing the script. The shebang should be "#!/usr/bin/env python3".
• Import the print function from the __future__ module to ensure compatibility with both Python 2 and Python 3. The import statement should be "from __future__ import print_function".
• Add a docstring at the beginning of the file to describe its purpose. The docstring should be """This script prints 'Hello, World!' when executed.""".
• Write a print statement to print "Hello, World!". The print statement should be "print('Hello, World!')".

• test.py ✅ Sandbox ran successfully

The following are the logs from running the sandbox:

Sandbox logs for aa90f8b ✓

trunk fmt test.py || return 0 1/2 ✓

 ✔ Formatted test.py
Re-checking autofixed files...


Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 3 linters

trunk check --fix --print-failures test.py 2/2 ✓

Checked 1 file
✔ No issues

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Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/create-test-file.

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