class_docstr: PyTorch implementation containing all the modelling needed for your NLP task. Combines 2 language
  models for representation of 2 sequences and a prediction head. Allows for gradient flow back to the 2 language model components.
class_docstr_tok: ["PyTorch","implementation","containing","all","the","modelling","needed","for","your","NLP","task",".","Combines","2","language","models","for","representation","of","2","sequences","and","a","prediction","head",".","Allows","for","gradient","flow","back","to","the","2","language","model","components","."]
code: def get_language(self):
      return (
          self.language_model1.language,
          self.language_model2.language,
      )
  
imports: import copy
  import json
  import logging
  import os
  from argparse import Namespace
  from pathlib import Path
  import multiprocessing
  import numpy
  import torch
  from torch import nn
  from farm.data_handler.processor import TextSimilarityProcessor
  from farm.data_handler.data_silo import DataSilo
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import PredictionHead, TextSimilarityHead
  from farm.modeling.tokenization import Tokenizer
  from farm.utils import MLFlowLogger, stack
label_desc: Use image node_id 11 for calling the BiAdaptiveModel obj's underlying member method code with example usage: obj.get_language() and returns: self,  self