class_docstr: Store the prompt to be sampled to generate the examples
  read a json file with the following format:
  [
      {
          "user_input" : "",
      } ,
      ...
  ]
  Where:
      user_input: is the input of the user or directly the input of the user
          with the memory preappended (i.e. user_input + memory)
class_docstr_tok: ["Store","the","prompt","to","be","sampled","to","generate","the","examples","read","a","json","file","with","the","following","format",":","[","{","``","user_input","''",":","``","''",",","}",",","...","]","Where",":","user_input",":","is","the","input","of","the","user","or","directly","the","input","of","the","user","with","the","memory","preappended","(","i.e",".","user_input","+","memory",")"]
docstr: Sample n examples from the data
  
  Args:
      n (int): Number of examples to sample
code: def sample(self, n: int) -> List:
      """Sample n examples from the data
  
      Args:
          n (int): Number of examples to sample
      """
      return random.sample(self.data, n)
  
imports: import json
  import os
  import random
  from collections import deque, namedtuple
  import deepspeed
  import torch
  import torch.distributed
  from accelerate import Accelerator
  from beartype import beartype
  from beartype.typing import Deque, List, Tuple, Union
  from deepspeed.runtime.engine import DeepSpeedEngine
  from torch.utils.data import DataLoader, Dataset
  from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
  from chatllama.rlhf.actor import ActorModel
  from chatllama.rlhf.config import Config, ConfigActor, ConfigCritic, ConfigReward
  from chatllama.rlhf.model_list import hf_models
  from chatllama.rlhf.model_loader import ModelLoader
  from chatllama.rlhf.reward import RewardModel, CriticModel
  from chatllama.rlhf.utils import TrainingStats, ConversationLog
label_desc: Use image node_id 2 for calling the ExamplesSampler obj's underlying member method code with example usage: obj.sample(n) and returns: random