code: def __init__(self, path, pad_idx, tokenizer, seq_len, strict=False):
      print_rank_0(
          "> building lambada dataset from {} ...".format(path)
      )
      self.seq_len = seq_len
      self.pad_idx = pad_idx
      self.tokenizer = tokenizer
      self.strict = strict
  
      self.tokens = []
      self.labels = []
      with open(path, "r") as f:
          for line in f.readlines():
              text = json.loads(line)["text"]
              tokens, labels = self.get_tokens(text)
              self.tokens.append(tokens)
              self.labels.append(labels)
  
imports: import json
  import math
  import numpy
  import torch
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_tokenizer
  from .detokenizer import get_detokenizer
label_desc: Use image node_id 1 to create a new _LambadaDataset object from inherited base classes: torch.utils.data with example: obj = _LambadaDataset(path, pad_idx, tokenizer, seq_len, strict)