docstr: Plots the learning rate range test.
  Arguments:
      skip_start (int, optional): number of batches to trim from the start.
          Default: 10.
      skip_end (int, optional): number of batches to trim from the start.
          Default: 5.
      log_lr (bool, optional): True to plot the learning rate in a logarithmic
          scale; otherwise, plotted in a linear scale. Default: True.
      show_lr (float, optional): if set, adds a vertical line to visualize the
          specified learning rate. Default: None.
      ax (matplotlib.axes.Axes, optional): the plot is created in the specified
          matplotlib axes object and the figure is not be shown. If `None`, then
          the figure and axes object are created in this method and the figure is
          shown . Default: None.
  Returns:
      The matplotlib.axes.Axes object that contains the plot.
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code: def plot(
      self,
      skip_start=10,
      skip_end=5,
      log_lr=True,
      show_lr=None,
      ax=None,
  ):
      """Plots the learning rate range test.
      Arguments:
          skip_start (int, optional): number of batches to trim from the start.
              Default: 10.
          skip_end (int, optional): number of batches to trim from the start.
              Default: 5.
          log_lr (bool, optional): True to plot the learning rate in a logarithmic
              scale; otherwise, plotted in a linear scale. Default: True.
          show_lr (float, optional): if set, adds a vertical line to visualize the
              specified learning rate. Default: None.
          ax (matplotlib.axes.Axes, optional): the plot is created in the specified
              matplotlib axes object and the figure is not be shown. If `None`, then
              the figure and axes object are created in this method and the figure is
              shown . Default: None.
      Returns:
          The matplotlib.axes.Axes object that contains the plot.
      """
  
      if skip_start < 0:
          raise ValueError("skip_start cannot be negative")
      if skip_end < 0:
          raise ValueError("skip_end cannot be negative")
      if show_lr is not None and not isinstance(show_lr, float):
          raise ValueError("show_lr must be float")
  
      # Get the data to plot from the history dictionary. Also, handle skip_end=0
      # properly so the behaviour is the expected
      lrs = self.history["lr"]
      losses = self.history["loss"]
      if skip_end == 0:
          lrs = lrs[skip_start:]
          losses = losses[skip_start:]
      else:
          lrs = lrs[skip_start:-skip_end]
          losses = losses[skip_start:-skip_end]
  
      # Create the figure and axes object if axes was not already given
      fig = None
      if ax is None:
          fig, ax = plt.subplots()
  
      # Plot loss as a function of the learning rate
      ax.plot(lrs, losses)
      if log_lr:
          ax.set_xscale("log")
      ax.set_xlabel("Learning rate")
      ax.set_ylabel("Loss")
  
      if show_lr is not None:
          ax.axvline(x=show_lr, color="red")
  
      # Show only if the figure was created internally
      if fig is not None:
          plt.show()
  
      return ax
  
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 13 for calling the BertLearner obj's underlying member method code with example usage: obj.plot(skip_start, skip_end, log_lr, show_lr, ax) and returns: ax