class_docstr: PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
  model and a prediction head. Allows for gradient flow back to the language model component.
class_docstr_tok: ["PyTorch","implementation","containing","all","the","modelling","needed","for","your","NLP","task",".","Combines","a","language","model","and","a","prediction","head",".","Allows","for","gradient","flow","back","to","the","language","model","component","."]
docstr: Logs paramteres to generic logger MlLogger
code: def log_params(self):
      """
      Logs paramteres to generic logger MlLogger
      """
      params = {
          "lm_type": self.language_model.__class__.__name__,
          "lm_name": self.language_model.name,
          "prediction_heads": ",".join(
              [
                  head.__class__.__name__
                  for head in self.prediction_heads
              ]
          ),
          "lm_output_types": ",".join(self.lm_output_types),
      }
      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 pathlib import Path
  import multiprocessing
  import numpy
  import torch
  from torch import nn
  from transformers import AutoConfig
  from transformers.convert_graph_to_onnx import convert, quantize
  from farm.data_handler.processor import Processor
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import PredictionHead, pick_single_fn
  from farm.utils import MLFlowLogger, stack
  import farm.conversion.transformers
label_desc: Use image node_id 11 for calling the AdaptiveModel obj's underlying member method code with example usage: obj.log_params() without return types