code: def __init__(
      self,
      data: BertDataBunch,
      model: nn.Module,
      pretrained_model_path,
      output_dir,
      metrics,
      device,
      logger,
      multi_gpu=True,
      is_fp16=True,
      loss_scale=0,
      warmup_steps=0,
      fp16_opt_level="O1",
      grad_accumulation_steps=1,
      multi_label=False,
      max_grad_norm=1.0,
      adam_epsilon=1e-8,
      logging_steps=100,
      freeze_transformer_layers=False,
      callbacks: Optional[List[TrainerCallback]] = None,
      learning_rate: float = 4e-5,
      optimizer_type: str = "lamb",
      epochs: int = 6,
  ):
      super(BertLearner, self).__init__(
          data,
          model,
          pretrained_model_path,
          output_dir,
          device,
          logger,
          multi_gpu,
          is_fp16,
          warmup_steps,
          fp16_opt_level,
          grad_accumulation_steps,
          max_grad_norm,
          adam_epsilon,
          logging_steps,
      )
  
      # Classification specific attributes
      self.multi_label = multi_label
      self.metrics = metrics
  
      # LR Finder
      self.history = {"lr": [], "loss": []}
      self.best_loss = None
      self.state_cacher = None
  
      self.scaler = (
          torch.cuda.amp.GradScaler() if is_fp16 is True else None
      )
  
      # Freezing transformer model layers
      if freeze_transformer_layers:
          for name, param in self.model.named_parameters():
              if name.startswith(data.model_type):
                  param.requires_grad = False
  
      # Optimizer
      self.optimizer_type = optimizer_type
      self.learning_rate = learning_rate
      self.optimizer = self.get_optimizer(
          self.learning_rate, optimizer_type=self.optimizer_type
      )
  
      train_dataloader = self.data.train_dl
      if self.max_steps > 0:
          t_total = self.max_steps
          self.epochs = (
              self.max_steps
              // len(train_dataloader)
              // self.grad_accumulation_steps
              + 1
          )
      else:
          t_total = (
              len(train_dataloader)
              // self.grad_accumulation_steps
              * epochs
          )
  
      self.lr_scheduler = self.get_scheduler(
          self.optimizer, t_total=t_total
      )
  
      # Callbacks
      callbacks = (
          DEFAULT_CALLBACKS
          if callbacks is None
          else DEFAULT_CALLBACKS + callbacks
      )
  
      self.callback_handler = CallbackHandler(
          callbacks=callbacks,
          model=self.model,
          tokenizer=self.data.tokenizer,
          optimizer=self.optimizer,
          lr_scheduler=self.lr_scheduler,
      )
  
      self.state = TrainerState()
      self.control = TrainerControl()
  
      self.training_arguments = TrainingArguments(
          str(output_dir),
          overwrite_output_dir=True,
          do_train=True,
          do_eval=True,
          evaluation_strategy="epoch",
          logging_strategy="epoch",
          per_device_train_batch_size=data.batch_size_per_gpu,
          per_device_eval_batch_size=data.batch_size_per_gpu * 2,
          gradient_accumulation_steps=grad_accumulation_steps,
          adam_epsilon=adam_epsilon,
          max_grad_norm=max_grad_norm,
          warmup_steps=warmup_steps,
          logging_dir=str(tensorboard_dir),
          logging_steps=logging_steps,
          fp16=is_fp16,
          fp16_opt_level=fp16_opt_level,
          save_steps=save_steps,
      )
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch, InputExample, InputFeatures
  from tqdm.autonotebook import tqdm
  import matplotlib.pyplot
  from .learner_util import Learner
  from torch import nn
  from typing import List, Optional
  from .modeling import BertForMultiLabelSequenceClassification, XLNetForMultiLabelSequenceClassification, RobertaForMultiLabelSequenceClassification, DistilBertForMultiLabelSequenceClassification, CamembertForMultiLabelSequenceClassification, AlbertForMultiLabelSequenceClassification, ElectraForMultiLabelSequenceClassification, FlaubertForMultiLabelSequenceClassification
  from .bert_layers import BertLayerNorm
  from fastprogress.fastprogress import master_bar, progress_bar
  import torch
  import pandas
  import numpy
  from sklearn.metrics import roc_curve, auc
  from pathlib import Path
  from torch.optim.lr_scheduler import _LRScheduler, Optimizer
  from tensorboardX import SummaryWriter
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer, AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer, FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer
  from transformers import AutoModelForSequenceClassification, AutoConfig
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 2 to create a new BertLearner object from inherited base classes: Learner with example: obj = BertLearner(data, model, pretrained_model_path, output_dir, metrics, device, logger, multi_gpu, is_fp16, loss_scale, warmup_steps, fp16_opt_level, grad_accumulation_steps, multi_label, max_grad_norm, adam_epsilon, logging_steps, freeze_transformer_layers, callbacks, learning_rate, optimizer_type, epochs)