code: def initialize_seed(seed: int, env):
      pl.seed_everything(seed)
      env.seed(seed)
      env.action_space.seed(seed)
  
imports: import json
  import logging
  from typing import Optional
  import gym
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from reagent.data.spark_utils import call_spark_class, get_spark_session
  from reagent.gym.agents.agent import Agent
  from reagent.gym.envs import Gym
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.runners.gymrunner import evaluate_for_n_episodes
  from reagent.gym.utils import fill_replay_buffer
  from reagent.model_managers.union import ModelManager__Union
  from reagent.publishers.union import FileSystemPublisher, ModelPublisher__Union
  from reagent.replay_memory.circular_replay_buffer import ReplayBuffer
  from reagent.replay_memory.utils import replay_buffer_to_pre_timeline_df
  from .types import TableSpec
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_seed(seed, env) without return types