class_docstr:     Trainer for PPO algorithm.
  
  Args:
      strategy (Strategy): the strategy to use for training
      actor (Actor): the actor model in ppo algorithm
      critic (Critic): the critic model in ppo algorithm
      reward_model (RewardModel): the reward model in rlhf algorithm to make reward of sentences
      initial_model (Actor): the initial model in rlhf algorithm to generate reference logics to limit the update of actor
      actor_optim (Optimizer): the optimizer to use for actor model
      critic_optim (Optimizer): the optimizer to use for critic model
      kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss
      train_batch_size (int, defaults to 8): the batch size to use for training
      buffer_limit (int, defaults to 0): the max_size limitation of buffer
      buffer_cpu_offload (bool, defaults to True): whether to offload buffer to cpu
      eps_clip (float, defaults to 0.2): the clip coefficient of policy loss
      vf_coef (float, defaults to 1.0): the coefficient of value loss
      ptx_coef (float, defaults to 0.9): the coefficient of ptx loss
      value_clip (float, defaults to 0.4): the clip coefficient of value loss
      sample_buffer (bool, defaults to False): whether to sample from buffer
      dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader
      offload_inference_models (bool, defaults to True): whether to offload inference models to cpu during training process
      callbacks (List[Callback], defaults to []): the callbacks to call during training process
      generate_kwargs (dict, optional): the kwargs to use while model generating
code: def _learn(self, update_step: int):
      if self.offload_inference_models:
          self.experience_maker.initial_model.to("cpu")
          self.experience_maker.reward_model.to("cpu")
  
      # buffer may be empty at first, we should rebuild at each training
      if self.sample_buffer:
          experience = self.data_buffer.sample()
          self._on_learn_batch_start()
          experience.to_device(self.device)
          self._training_step(experience)
          self._on_learn_batch_end(experience)
      else:
          if isinstance(self.dataloader.sampler, DistributedSampler):
              self.dataloader.sampler.set_epoch(update_step)
          pbar = tqdm(
              self.dataloader,
              desc=f"Train epoch [{update_step + 1}]",
              disable=not is_rank_0(),
          )
          for experience in pbar:
              self._on_learn_batch_start()
              experience.to_device(self.device)
              self._training_step(experience)
              self._on_learn_batch_end(experience)
  
imports: from typing import Dict, List, Optional
  from coati.experience_buffer import NaiveExperienceBuffer
  from coati.experience_maker import Experience, NaiveExperienceMaker
  from coati.models.base import Actor, Critic, RewardModel, get_base_model
  from coati.models.loss import GPTLMLoss, PolicyLoss, ValueLoss
  from coati.models.utils import calc_action_log_probs
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader, DistributedSampler
  from tqdm import tqdm
  from transformers import PreTrainedTokenizerBase
  from colossalai.utils import get_current_device
  from .base import OnPolicyTrainer
  from .callbacks import Callback
  from .strategies import GeminiStrategy, Strategy
  from .utils import CycledDataLoader, is_rank_0, to_device
label_desc: Use image node_id 5 for calling the PPOTrainer obj's underlying member method code with example usage: obj._learn(update_step) without return types