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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def play_env_problem_randomly(env_problem, num_steps):
 """Plays the env problem by randomly sampling actions for `num_steps`."""

  # Reset all environments.
  env_problem.reset()

  # Play all environments, sampling random actions each time.
  for _ in range(num_steps):
    # Sample batch_size actions from the action space and stack them.
    actions = np.stack([env_problem.action_space.sample() for _ in range(
        env_problem.batch_size)])

    # Execute actions, observations are stored in `env_problem`.
    _, _, dones, _ = env_problem.step(actions)

    # Get the indices where we are done and reset those.
    env_problem.reset(indices=done_indices(dones))