code: def _get_current_lr_and_momentum(self, batch_count):
      current_learning_rate = [
          group["lr"] for group in self.optimizer.param_groups
      ]
      momentum = [
          group["momentum"] if "momentum" in group else 0
          for group in self.optimizer.param_groups
      ]
      lr_info = " - lr: " + ",".join(
          [f"{m:.6f}" for m in current_learning_rate]
      )
      momentum_info = " - momentum: " + ",".join(
          [f"{m:.6f}" for m in momentum]
      )
      self._record(
          MetricRecord.scalar_list(
              "learning_rate", current_learning_rate, batch_count
          )
      )
      self._record(
          MetricRecord.scalar_list(
              ("optimizer", "momentum"), momentum, batch_count
          )
      )
      return lr_info, momentum_info
  
imports: import contextlib
  import inspect
  import logging
  import os
  import random
  import time
  import warnings
  from inspect import signature
  from pathlib import Path
  from typing import List, Optional, Tuple, Type, Union
  import torch
  from torch.optim.sgd import SGD
  from torch.utils.data.dataset import ConcatDataset
  import flair
  import flair.nn
  from flair.data import Corpus, Dictionary, _len_dataset
  from flair.datasets import DataLoader
  from flair.samplers import FlairSampler
  from flair.trainers.plugins import AnnealingPlugin, CheckpointPlugin, LinearSchedulerPlugin, LogFilePlugin, LossFilePlugin, MetricName, MetricRecord, Pluggable, ReduceTransformerVocabPlugin, TrainerPlugin, TrainingInterrupt, WeightExtractorPlugin
  from flair.training_utils import identify_dynamic_embeddings, log_line, store_embeddings
label_desc: Use image node_id 10 for calling the ModelTrainer obj's underlying member method code with example usage: obj._get_current_lr_and_momentum(batch_count) and returns: lr_info,  momentum_info