docstr: Take N samples from the dataset
  
  Args:
      conversations (List[Dict]): list of conversations
      n (int): number of samples to take randomly
  
  Returns:
      List[Dict]: list of N samples
docstr_tok: ["Take","N","samples","from","the","dataset","Args",":","conversations","(","List","[","Dict","]",")",":","list","of","conversations","n","(","int",")",":","number","of","samples","to","take","randomly","Returns",":","List","[","Dict","]",":","list","of","N","samples"]
code: def take_n_samples(
      conversations: List[Dict],
      n: int,
  ) -> List[Dict]:
      """Take N samples from the dataset
  
      Args:
          conversations (List[Dict]): list of conversations
          n (int): number of samples to take randomly
  
      Returns:
          List[Dict]: list of N samples
      """
  
      # sample N number of index from 0 to len(conversations)
      indexes = np.random.choice(
          len(conversations), size=n, replace=False
      )
      # take the samples
      conversations = [conversations[i] for i in indexes]
      return conversations
  
imports: import json
  import os
  import numpy
  from beartype.typing import Dict, List, Union
  from datasets import load_dataset
  from chatllama.rlhf.config import Config, ConfigActor, ConfigReward
  from chatllama.rlhf.reward import RewardModel, CriticModel
  from chatllama.rlhf.actor import ActorModel
label_desc: Use image node_id 3 for calling the BaseDataset obj's underlying member method code with example usage: obj.take_n_samples(conversations, n) and returns: conversations