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","."]
docstr: Logs paramteres to generic logger MlLogger
docstr_tok: ["Logs","paramteres","to","generic","logger","MlLogger"]
code: def log_params(self):
      """
      Logs paramteres to generic logger MlLogger
      """
      params = {
          "lm1_type": self.language_model1.__class__.__name__,
          "lm1_name": self.language_model1.name,
          "lm1_output_types": ",".join(self.lm1_output_types),
          "lm2_type": self.language_model2.__class__.__name__,
          "lm2_name": self.language_model2.name,
          "lm2_output_types": ",".join(self.lm2_output_types),
          "prediction_heads": ",".join(
              [
                  head.__class__.__name__
                  for head in self.prediction_heads
              ]
          ),
      }
      try:
          MlLogger.log_params(params)
      except Exception as e:
          logger.warning(f"ML logging didn't work: {e}")
  
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 9 for calling the BiAdaptiveModel obj's underlying member method code with example usage: obj.log_params() without return types