code: def obs_to_torch(obs: np.ndarray) -> torch.Tensor:
      """Scale observations from `[0, 255]` to `[0, 1]`"""
      return (
          torch.tensor(obs, dtype=torch.float32, device=device) / 255.0
      )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/rl/dqn/experiment.py
imports: import numpy
  import torch
  from labml import tracker, experiment, logger, monit
  from labml.internal.configs.dynamic_hyperparam import FloatDynamicHyperParam
  from labml_helpers.schedule import Piecewise
  from labml_nn.rl.dqn import QFuncLoss
  from labml_nn.rl.dqn.model import Model
  from labml_nn.rl.dqn.replay_buffer import ReplayBuffer
  from labml_nn.rl.game import Worker
label_desc: Use image node_id 1 for calling a global function with example usage: obs_to_torch(obs) and returns: unknown