class_docstr: Special purpose Data object which assumes a single text tensorizer.  Packs
  tokens into a square batch with no padding.  Used for LM training. The object
  also takes in an optional language argument which is used for cross-lingual
  LM training.
class_docstr_tok: ["Special","purpose","Data","object","which","assumes","a","single","text","tensorizer",".","Packs","tokens","into","a","square","batch","with","no","padding",".","Used","for","LM","training",".","The","object","also","takes","in","an","optional","language","argument","which","is","used","for","cross-lingual","LM","training","."]
code: def from_config(
      cls,
      config: Config,
      schema: Dict[str, Type],
      tensorizers: Dict[str, Tensorizer],
      language: Optional[str] = None,
      rank: int = 0,
      world_size: int = 1,
      init_tensorizers: Optional[bool] = True,
  ):
      return super(PackedLMData, cls).from_config(
          config,
          schema,
          tensorizers,
          rank,
          world_size,
          language=language,
          max_seq_len=config.max_seq_len,
          init_tensorizers=init_tensorizers,
      )
  
file_path: pytext/pytext/data/packed_lm_data.py
imports: from typing import Dict, List, Optional, Type
  from pytext.common.constants import Stage
  from pytext.data import Batcher, Data
  from pytext.data.bert_tensorizer import BERTTensorizerBase
  from pytext.data.data import RowData
  from pytext.data.sources import DataSource
  from pytext.data.tensorizers import Tensorizer, TokenTensorizer
label_desc: Use image node_id 1 for calling the PackedLMData obj's underlying member method code with example usage: obj.from_config(cls, config, schema, tensorizers, language, rank, world_size, init_tensorizers) and returns: super