class_docstr: Linearly increases the learning rate between two boundaries over a number of
  iterations.
  Arguments:
      optimizer (torch.optim.Optimizer): wrapped optimizer.
      end_lr (float): the final learning rate.
      num_iter (int): the number of iterations over which the test occurs.
      last_epoch (int, optional): the index of last epoch. Default: -1.
class_docstr_tok: ["Linearly","increases","the","learning","rate","between","two","boundaries","over","a","number","of","iterations",".","Arguments",":","optimizer","(","torch.optim.Optimizer",")",":","wrapped","optimizer",".","end_lr","(","float",")",":","the","final","learning","rate",".","num_iter","(","int",")",":","the","number","of","iterations","over","which","the","test","occurs",".","last_epoch","(","int",",","optional",")",":","the","index","of","last","epoch",".","Default",":","-1","."]
code: def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
      self.end_lr = end_lr
  
      if num_iter <= 1:
          raise ValueError("`num_iter` must be larger than 1")
      self.num_iter = num_iter
  
      super(LinearLR, self).__init__(optimizer, last_epoch)
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch
  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 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, TrainingArguments
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 1 to create a new LinearLR object from inherited base classes: _LRScheduler with example: obj = LinearLR(optimizer, end_lr, num_iter, last_epoch)