class_docstr: ```
  Abstract class used to tune and train Keras models. The fit method is
  an abstract method and must be implemented by subclasses.
  ```
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docstr: ```
  Plots the loss vs. learning rate to help identify
  The maximal learning rate associated with a falling loss.
  The nskip_beginning and n_skip_end arguments can be used
  to "zoom in" on the plot.
  Args:
      n_skip_beginning(int): number of batches to skip on the left.
      n_skip_end(int):  number of batches to skip on the right.
      suggest(bool): will highlight numerical estimate
                     of best lr if True - methods adapted from fastai
      return_fig(bool): If True, return matplotlib.figure.Figure
  Returns:
    matplotlib.figure.Figure if return_fig else None
  ```
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code: def lr_plot(
      self,
      n_skip_beginning=10,
      n_skip_end=5,
      suggest=False,
      return_fig=False,
  ):
      """
      ```
      Plots the loss vs. learning rate to help identify
      The maximal learning rate associated with a falling loss.
      The nskip_beginning and n_skip_end arguments can be used
      to "zoom in" on the plot.
      Args:
          n_skip_beginning(int): number of batches to skip on the left.
          n_skip_end(int):  number of batches to skip on the right.
          suggest(bool): will highlight numerical estimate
                         of best lr if True - methods adapted from fastai
          return_fig(bool): If True, return matplotlib.figure.Figure
      Returns:
        matplotlib.figure.Figure if return_fig else None
      ```
      """
      # dep_fix: bug in TF 2.2 and 2.3
      if version.parse(tf.__version__) > version.parse(
          "2.1"
      ) and version.parse(tf.__version__) < version.parse("2.4"):
          if n_skip_end == 5:
              n_skip_end = 10
  
      if self.lr_finder is None or not self.lr_finder.find_called():
          raise ValueError("Please call lr_find first.")
      return self.lr_finder.plot_loss(
          n_skip_beginning=n_skip_beginning,
          n_skip_end=n_skip_end,
          suggest=suggest,
          return_fig=return_fig,
      )
  
imports: from .None import utils
  from .graph.predictor import LinkPredictor, NodePredictor
  from .graph.preprocessor import LinkPreprocessor, NodePreprocessor
  from .imports import *
  from .lroptimize.lrfinder import *
  from .tabular.predictor import TabularPredictor
  from .tabular.preprocessor import TabularPreprocessor
  from .text.ner.predictor import NERPredictor
  from .text.ner.preprocessor import NERPreprocessor
  from .text.predictor import TextPredictor
  from .text.preprocessor import BERTPreprocessor, TextPreprocessor, TransformersPreprocessor
  from .vision.predictor import ImagePredictor
  from .vision.preprocessor import ImagePreprocessor
label_desc: Use image node_id 21 for calling the Learner obj's underlying member method code with example usage: obj.lr_plot(n_skip_beginning, n_skip_end, suggest, return_fig) and returns: self