code: def get_scheduler_and_optimizer(
      self, parameters, train_tensor_data, logger
  ):
      model = self.model
  
      num_train_optimization_steps = (
          int(
              len(train_tensor_data)
              / parameters["train_batch_size"]
              / parameters["gradient_accumulation_steps"]
          )
          * parameters["num_train_epochs"]
      )
      num_warmup_steps = int(
          num_train_optimization_steps * parameters["warmup_proportion"]
      )
      param_optimizer = list(model.named_parameters())
  
      param_optimizer = [n for n in param_optimizer]
  
      no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p
                  for n, p in param_optimizer
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.01,
          },
          {
              "params": [
                  p
                  for n, p in param_optimizer
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
  
      optimizer = AdamW(
          optimizer_grouped_parameters,
          lr=parameters["learning_rate"],
          correct_bias=False,
      )
  
      scheduler = WarmupLinearSchedule(
          optimizer,
          warmup_steps=num_warmup_steps,
          t_total=num_train_optimization_steps,
      )
  
      logger.info(
          "  Num optimization steps = %d", num_train_optimization_steps
      )
      logger.info("  Num warmup steps = %d", num_warmup_steps)
      return optimizer, scheduler
  
imports: import torch
  import os
  import numpy
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from tqdm import tqdm
  from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
  from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
label_desc: Use image node_id 3 for calling the BertReranker obj's underlying member method code with example usage: obj.get_scheduler_and_optimizer(parameters, train_tensor_data, logger) and returns: optimizer,  scheduler