class_docstr: Implements the Slanted Triangular Learning Rate schedule with optional gradual
  unfreezing and discriminative fine-tuning. The schedule corresponds to first
  linearly increasing the learning rate over some number of epochs, and then linearly
  decreasing it over the remaining epochs.
  
  If we gradually unfreeze, then in the first epoch of training, only the top
  layer is trained; in the second epoch, the top two layers are trained, etc.
  During freezing, the learning rate is increased and annealed over one epoch.
  After freezing finished, the learning rate is increased and annealed over
  the remaining training iterations.
  
  Note that with this schedule, early stopping should typically be avoided.
  
  Registered as a `LearningRateScheduler` with name "slanted_triangular".
  
  # Parameters
  
  optimizer : `torch.optim.Optimizer`
      This argument does not get an entry in a configuration file for the object.
  num_epochs : `int`, required.
      The total number of epochs for which the model should be trained.
  num_steps_per_epoch : `Optional[int]`, optional (default = `None`)
      The number of steps (updates, batches) per training epoch.
  cut_frac : `float`, optional (default = `0.1`).
      The fraction of the steps to increase the learning rate.
  ratio : `float`, optional (default = `32`).
      The ratio of the smallest to the (largest) base learning rate.
  gradual_unfreezing : `bool`, optional (default = `False`).
      Whether gradual unfreezing should be used.
  discriminative_fine_tuning : `bool`, optional (default = `False`).
      Whether discriminative fine-tuning (different learning rates per layer)
      are used.
  decay_factor : `float`, optional (default = `0.38`).
      The decay factor by which the learning rate is reduced with
      discriminative fine-tuning when going a layer deeper.
code: def get_values(self):
      # get the actual number of batches per epoch seen in training
      if len(self.batch_num_total_epoch_end) > 1:
          # have finished an epoch
          actual_num_steps_per_epoch = int(
              self.batch_num_total_epoch_end[-1]
              / (len(self.batch_num_total_epoch_end) - 1)
          )
      else:
          actual_num_steps_per_epoch = max(
              self.num_steps_per_epoch or 1, self.last_batch_num_total
          )
  
      if self.freezing_current:
          # if we are still freezing layers, we restrict the schedule to the current epoch
          num_steps = actual_num_steps_per_epoch
          step = min(
              self.last_batch_num_total
              - self.batch_num_total_epoch_end[-1],
              num_steps,
          )
      else:
          # otherwise we use the schedule for the rest of training
          if not self.gradual_unfreezing:
              frozen_steps = 0
          else:
              num_frozen_epochs = len(self.optimizer.param_groups) - 2
              frozen_steps = self.batch_num_total_epoch_end[
                  num_frozen_epochs
              ]
          num_steps = (
              self.num_epochs * actual_num_steps_per_epoch
              - frozen_steps
          )
          step = min(
              self.last_batch_num_total - frozen_steps, num_steps
          )
      cut = int(num_steps * self.cut_frac)
      prop = (
          step / cut
          if step < cut
          else 1 - (step - cut) / (num_steps - cut)
      )
      return [
          lr * (1 + prop * (self.ratio - 1)) / self.ratio
          for lr in self.base_values
      ]
  
file_path: allennlp/allennlp/training/learning_rate_schedulers/slanted_triangular.py
imports: import logging
  from typing import List, Optional
  import torch
  from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
label_desc: Use image node_id 4 for calling the SlantedTriangular obj's underlying member method code with example usage: obj.get_values() and returns: unknown