code: def from_pretrained_model(
      dataBunch,
      pretrained_path,
      output_dir,
      metrics,
      device,
      logger=logging.getLogger(__name__),
      finetuned_wgts_path=None,
      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,
      pos_weight=None,
      weight=None,
      callbacks: Optional[List[TrainerCallback]] = None,
      learning_rate: float = 4e-5,
      optimizer_type: str = "lamb",
      epochs: int = 6,
  ):
      if is_fp16 and (IS_AMP_AVAILABLE is False):
          logger.debug(
              "Apex not installed. switching off FP16 training"
          )
          is_fp16 = False
  
      model = load_model(
          dataBunch,
          pretrained_path,
          finetuned_wgts_path,
          device,
          multi_label,
          pos_weight,
          weight,
      )
  
      return BertLearner(
          dataBunch,
          model,
          str(pretrained_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,
      )
  
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 1 for calling the BertLearner obj's underlying member method code with example usage: obj.from_pretrained_model(dataBunch, pretrained_path, output_dir, metrics, device, logger, finetuned_wgts_path, 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, pos_weight, weight, callbacks, learning_rate, optimizer_type, epochs) and returns: BertLearner