class_docstr: Custom Dataset wrapping language modeling sequences.
  
  Each sample will be retrieved by indexing the list of token_ids and their corresponding lengths.
  
  Input:
  ------
      params: `NameSpace` parameters
      data: `List[np.array[int]]
code: def __init__(self, params, data):
      self.params = params
  
      self.token_ids = np.array(data)
      self.lengths = np.array([len(t) for t in data])
  
      self.check()
      self.remove_long_sequences()
      self.remove_empty_sequences()
      self.remove_unknown_sequences()
      self.check()
      self.print_statistics()
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/distillation/lm_seqs_dataset.py
imports: import numpy
  import torch
  from torch.utils.data import Dataset
  from utils import logger
label_desc: Use image node_id 1 to create a new LmSeqsDataset object from inherited base classes: Dataset with example: obj = LmSeqsDataset(params, data)