class_docstr: The dataset class for prompt-tuning or p-tuning pretrained GPT models.
  
  Args:
      data (list[strings], list[dicts]): (1) paths to .jsonl or .json files, (2) dict objects corresponding to each input example
      tokenizer (tokenizer): Tokenizer from frozen language model
      virtual_prompt_source (Enum): Either VirtualPromptSource.NO_PROMPTS or VirtualPromptSource.PROMPT_ENCODER
      task_templates (dict): Dictionary containing all task template information needed to format prompts. Created in the GPTPromptLearningModel class.
      pseudo_tokens (list[strings]): A list of virtual prompt token placeholders e.g [<prompt_1>, <prompt_2>, ...] up to max num virtual tokens
      pad_token_id (int): ID of pad token from tokenizer
      max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated.
      min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. 
      add_bos (bool): Whether to add a beginning of sentence token to each data example
      add_eos (bool): Whether to add an end of sentence token to each data example
      for_train (bool): Whether you're creating a dataset for training or inference
      tokens_to_generate (int): (inference only) Number of tokens to generate during inference
code: def _ceil_to_nearest(self, n, m):
      return (n + m - 1) // m * m
  
imports: import json
  import os
  import pickle
  import torch
  from tqdm.auto import tqdm
  from nemo.collections.nlp.modules.common import VirtualPromptSource
  from nemo.collections.nlp.modules.common.megatron.utils import build_position_ids
  from nemo.core import Dataset
  from nemo.utils import AppState, logging
label_desc: Use image node_id 11 for calling the GPTPromptLearningDataset obj's underlying member method code with example usage: obj._ceil_to_nearest(n, m) and returns: unknown