code: def slanted_triangular(
      step,
      total_train_steps,
      cut_fraction=0.1,
      ratio=32,
      max_learning_rate=0.01,
      start_step=0,
  ):
      """Triangular learning rate with short increase and long decay.
  
      TODO(noam): add minimum_value arguments to linear_decay() and linear_warmup()
      so that this function can be replaced.
  
      Taken from "Universal Language Model Fine-tuning for Text Classification",
      see https://arxiv.org/abs/1801.06146. Default parameters are those specified
      in the paper.
  
      Args:
        step: a tf.scalar representing the step we want the learning rate for.
        total_train_steps: a number, the total number of training steps.
        cut_fraction: a number between 0 and 1, fraction of iterations for which we
          are increasing the learning rate.
        ratio: a number greater than 1, the ratio from the smallest learning rate to
          the max learning rate.
        max_learning_rate: a number, the highest learning rate reached during
          training.
        start_step: a number, the step training starts at. Useful when fine-tuning
          from a checkpoint that hasn't had its global step reset.
  
      Returns:
        a tf.Scalar, the learning rate for the step.
      """
      train_steps = float(total_train_steps)
      start_step = float(start_step)
      step_num = tf.cast(step, tf.float32) - start_step
      cut = math.floor(train_steps * cut_fraction)
      p = tf.cond(
          step_num < cut,
          lambda: step_num / cut,
          lambda: 1 - (step_num - cut) / (cut * (1 / cut_fraction - 1)),
      )
      return max_learning_rate * (1 + p * (ratio - 1)) / ratio
  
file_path: mesh/mesh_tensorflow/transformer/learning_rate_schedules.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import gin
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling a global function with example usage: slanted_triangular(step, total_train_steps, cut_fraction, ratio, max_learning_rate, start_step) and returns: unknown