class_docstr: The dataset class for fine tune RETRO 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
      task_templates (dict): Dictionary containing all task template information needed to format prompts. Created in the GPTPromptLearningModel class.
      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
docstr: Process a single example from the dataset into IDs and other T0-related metadata.
docstr_tok: ["Process","a","single","example","from","the","dataset","into","IDs","and","other","T0-related","metadata","."]
code: def _process_example(self, example):
      """
      Process a single example from the dataset into IDs and other T0-related metadata.
      """
      question = example["question"].strip()
      tokenized_input = self.tokenizer.text_to_ids(
          f"question: {question}\n"
      )
      # add a space between input and output
      if "answers" in example:
          # sample one answer from answers
          answer = sample(example["answers"], 1)[0].strip()
          tokenized_output = self.tokenizer.text_to_ids(
              f"answer: {answer}"
          )
      else:
          tokenized_output = self.tokenizer.text_to_ids("answer: ")
  
      bos_id = self.tokenizer.bos_id
      if self.add_bos:
          tokenized_input = [bos_id] + tokenized_input
      if self.add_eos:
          target = tokenized_output + [self.tokenizer.eos_id]
      else:
          target = tokenized_output
  
      # pad the question so 'answer:' coincides with the end of the first chunk of 64
      if len(tokenized_input) < 64:
          padding_length = 64 - len(tokenized_input)
          tokenized_input = [
              self.pad_token_id
          ] * padding_length + tokenized_input
  
      if len(tokenized_input) + len(target) > self.max_seq_length:
          cut_tokens = (
              len(tokenized_input) + len(target) - self.max_seq_length
          )
          if len(tokenized_input) - cut_tokens > 0:
              # cut the input by default
              tokenized_input = tokenized_input[
                  : len(tokenized_input) - cut_tokens
              ]
          elif len(target) - cut_tokens > 0:
              # cut the output
              target = target[: len(target) - cut_tokens]
          else:
              # cut both the input and output
              cut_input_tokens = (
                  len(tokenized_input) - 1
              )  # retain at least one token
              cut_output_tokens = cut_tokens - cut_input_tokens
              tokenized_input = tokenized_input[
                  : len(tokenized_input) - cut_input_tokens
              ]
              target = target[: len(target) - cut_output_tokens]
  
      chunks = []
      contexts = example["ctxs"]
      assert self.neighbors <= len(
          contexts
      ), f"specify {self.neighbors}, but only provide {len(contexts)} neighbors in the dataset"
      for neighbor in contexts[: self.neighbors]:
          tokens = self.tokenizer.text_to_ids(neighbor)
          tokens = tokens[:128]
          if len(tokens) < 128:
              tokens = tokens + [self.pad_token_id] * (
                  128 - len(tokens)
              )
          chunks.append(tokens)
  
      answer_start_idx = len(tokenized_input)
      input_ids = tokenized_input + target
      assert (
          len(input_ids) <= 128
      ), "cannot handle more than two chunks yet"
      chunks = (
          np.array(chunks)
          .reshape(1, self.neighbors, -1)
          .astype(np.int64)
      )
      results = (input_ids, answer_start_idx, chunks)
      return results
  
file_path: NeMo/nemo/collections/nlp/data/language_modeling/megatron/retro_fine_tune_dataset.py
imports: from random import sample
  import numpy
  import torch
  from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import get_samples_mapping
  from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import JSONLMemMapDataset
  from nemo.core import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the RetroQAFineTuneDataset obj's underlying member method code with example usage: obj._process_example(example) and returns: results