class_docstr: Dataset for unlearning span corruption (https://arxiv.org/abs/2104.08691) in T5 models.
  Corresponds to the prefix-LM objective in the T5 paper (Table 3 in https://arxiv.org/abs/1910.10683).
code: def __getitem__(self, idx):
      text = super()._get_text(idx)
      np_rng = np.random.RandomState(seed=(self.seed + idx))
      sample = T5LMAdaptedDataset.get_prefix_lm_sample(
          sample=text,
          max_seq_length_encoder=self.max_seq_length_encoder,
          max_seq_length_decoder=self.max_seq_length_decoder,
          np_rng=np_rng,
          tokenizer=self.tokenizer,
          pivot_distribution=LengthDistribution.uniform,
      )
      return sample
  
imports: import numpy
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_dataset import GPTDataset
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
label_desc: Use image node_id 3 for calling the T5LMAdaptedDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: sample