class_docstr: Configurations of the PPO model
class_docstr_tok: ["Configurations","of","the","PPO","model"]
code: def __init__(self):
      self.observation_space = None
      self.action_space = None
      self.num_envs = 0
      self.nsteps = 0
  
      self.ent_coef = 0.0
      self.lr = 3e-4
      self.vf_coef = 0.5
      self.max_grad_norm = 0.5
      self.gamma = 0.99
      self.lam = 0.95
      self.cliprange = 0.2
      self.embedding_size = None  # the embedding is for each action
  
      self.noptepochs = 4  # number of training epochs per update
      self.total_timesteps = 5000  # number of timesteps (i.e. number of actions taken in the environment)
      self.nminibatches = 4
  
imports: import copy
  import logging
  import numpy
  from gym import spaces
  from schema import Schema, Optional
  import nni
  from nni import ClassArgsValidator
  from nni.tuner import Tuner
  from nni.utils import OptimizeMode, extract_scalar_reward
  from .model import Model
  from .util import set_global_seeds
  from .policy import build_lstm_policy
label_desc: Use image node_id 1 to create a new ModelConfig object  with example: obj = ModelConfig()