class_docstr: Args:
      requests: List of prompts
      tokenizer: model tokenizer
      tokens_to_generate: int value denoting amount of tokens model should generate
      compute_logprobs: bool value denoting if model should generate tokens or compute logprobs
  Returns:
      data: class object
          {'data': tokens, 'tokens_to_generate': tokens_to_generate, 'compute_logprobs': compute_logprobs}
          * data: List of token's ids in respect to prompts
          * tokens_to_generate: int value denoting amount of tokens model should generate
          * compute_logprobs: bool value denoting if model should generate tokens or compute logprobs
class_docstr_tok: ["Args",":","requests",":","List","of","prompts","tokenizer",":","model","tokenizer","tokens_to_generate",":","int","value","denoting","amount","of","tokens","model","should","generate","compute_logprobs",":","bool","value","denoting","if","model","should","generate","tokens","or","compute","logprobs","Returns",":","data",":","class","object","{","'data","'",":","tokens",",","'tokens_to_generate","'",":","tokens_to_generate",",","'compute_logprobs","'",":","compute_logprobs","}","*","data",":","List","of","token","'s","ids","in","respect","to","prompts","*","tokens_to_generate",":","int","value","denoting","amount","of","tokens","model","should","generate","*","compute_logprobs",":","bool","value","denoting","if","model","should","generate","tokens","or","compute","logprobs"]
code: def __len__(self):
      return 1
  
file_path: NeMo/nemo/collections/nlp/data/language_modeling/megatron/request_dataset.py
imports: from typing import Dict, List
  import torch
  from torch.utils.data.dataset import Dataset
label_desc: Use image node_id 2 for calling the GPTRequestDataset obj's underlying member method code with example usage: obj.__len__() and returns: int