code: def from_config(
      cls,
      config: Config,
      schema: Dict[str, Type],
      tensorizers: Dict[str, Tensorizer],
      rank=0,
      world_size=1,
      init_tensorizers=True,
      **kwargs,
  ):
      return super().from_config(
          config,
          schema,
          tensorizers,
          rank,
          world_size,
          init_tensorizers,
          decoupled_source=config.decoupled_source,
          decoupled_target=config.decoupled_target,
          noisy_decoupling=config.noisy_decoupling,
          filter_target_ood_slots=config.filter_target_ood_slots,
          merge_source_vocab=config.merge_source_vocab,
          merge_vocab=config.merge_vocab,
          unk_token=config.unk_token,
          pad_token=config.pad_token,
          bos_token=config.bos_token,
          eos_token=config.eos_token,
          mask_token=config.mask_token,
          **kwargs,
      )
  
file_path: pytext/pytext/data/decoupled_data.py
imports: from typing import Dict, List, Optional, Type
  from .None import Batcher, Data
  from .data import RowData
  from .sources import DataSource
  from .tensorizers import Tensorizer
  from .utils import Vocabulary
label_desc: Use image node_id 1 for calling the DecoupledSeq2SeqData obj's underlying member method code with example usage: obj.from_config(cls, config, schema, tensorizers, rank, world_size, init_tensorizers) and returns: super