class_docstr: UL2 Dataset from https://arxiv.org/abs/2205.05131.
  Consists of three different objectives:
  1. Short span masking with small probabilities (ex: T5). Typically max ngram size of 5 with 0.15 mask prob.
  2. Extreme span masking with either large probabilities or large ngram sizes or both.
  3. Prefx-LM as in the T5 or LM-adapted T5 (prompt-tuning paper).
class_docstr_tok: ["UL2","Dataset","from","https",":","\/\/arxiv.org\/abs\/2205.05131",".","Consists","of","three","different","objectives",":","1",".","Short","span","masking","with","small","probabilities","(","ex",":","T5",")",".","Typically","max","ngram","size","of","5","with","0.15","mask","prob",".","2",".","Extreme","span","masking","with","either","large","probabilities","or","large","ngram","sizes","or","both",".","3",".","Prefx-LM","as","in","the","T5","or","LM-adapted","T5","(","prompt-tuning","paper",")","."]
code: def _prepend_mask_type_token(cls, tokenizer, sample, token):
      token_id = tokenizer.text_to_ids(token)
      assert len(token_id) == 1, token
      token_id = token_id[0]
      text_enc = np.concatenate([[token_id], sample["text_enc"]])
      sample["text_enc"] = text_enc
      if "enc_mask" in sample:
          sample["enc_mask"] = np.concatenate([[1], sample["enc_mask"]])
      return sample
  
file_path: NeMo/nemo/collections/nlp/data/language_modeling/megatron/ul2_dataset.py
imports: import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import create_extreme_masked_lm_predictions
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
  from nemo.collections.nlp.data.language_modeling.megatron.lm_adapted_t5_dataset import T5LMAdaptedDataset
  from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset
label_desc: Use image node_id 6 for calling the UL2Dataset obj's underlying member method code with example usage: obj._prepend_mask_type_token(cls, tokenizer, sample, token) and returns: sample