new_contents: # based on https://github.com/williamFalcon/hello/blob/main/hello.py
  import os
  import numpy as np
  from argparse import ArgumentParser
  import torch
  from torch.utils.tensorboard import SummaryWriter
  
  # add arguments 
  parser = ArgumentParser()
  parser.add_argument('--number', default=0, type=int)
  parser.add_argument('--food_item', default='burgers', type=str)
  parser.add_argument('--data', default=None, type=str)
  args = parser.parse_args()
  print(args)
  
  # fake tensorboard logs (fake loss)
  writer = SummaryWriter()
  offset = np.random.uniform(0, 5, 1)[0]
  for x in range(1, 10000):
      y = -np.log(x) + offset + (np.sin(x) * 0.1)
      writer.add_scalar('y=-log(x) + c + 0.1sin(x)', y, x)
      writer.add_scalar('fake_metric', -y, x)
  
  writer.close()
  
  # print data if available
  if args.data is not None:
      files = list(os.walk(args.data))
      print('-' * 50)
      print(f'DATA FOUND! {len(files)} files found at dataset {args.data}')
  
  # print GPUs, params and random tensors
  print('-' * 50)
  print(f'GPUS: There are {torch.cuda.device_count()} GPUs on this machine')
  print('-' * 50)
  print(f'PARAMS: I want to eat: {args.number} {args.food_item}')
  print('-' * 50)
  print('i can run any ML library like numpy, pytorch lightning, sklearn pytorch, keras, tensorflow')
  print('torch:', torch.rand(1), 'numpy', np.random.rand(1))
  
  # write some artifacts
  f = open("weights.pt", "a")
  f.write("fake weights")
  f.close()
  
subject: Create hello-grid.py