code: def get_lr_scheduler(self):
      get_schedule_func = arg_to_scheduler[self.hparams.lr_scheduler]
      scheduler = get_schedule_func(
          self.opt,
          num_warmup_steps=self.hparams.warmup_steps,
          num_training_steps=self.total_steps(),
      )
      scheduler = {
          "scheduler": scheduler,
          "interval": "step",
          "frequency": 1,
      }
      return scheduler
  
imports: import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  import pytorch_lightning
  from pytorch_lightning.utilities import rank_zero_info
  from transformers import AdamW, AutoConfig, AutoModel, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelWithLMHead, AutoTokenizer, PretrainedConfig, PreTrainedTokenizer
  from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup
  from transformers.utils.versions import require_version
label_desc: Use image node_id 3 for calling the BaseTransformer obj's underlying member method code with example usage: obj.get_lr_scheduler() and returns: scheduler