code: def learning_rate_schedule(
      global_step,
      warmup_steps,
      base_learning_rate,
      lr_scaling,
      train_steps,
  ):
      warmup_steps = int(round(warmup_steps))
      scaled_lr = base_learning_rate * lr_scaling
      if warmup_steps:
          learning_rate = global_step / warmup_steps * scaled_lr
      else:
          learning_rate = scaled_lr
  
      if global_step < warmup_steps:
          learning_rate = learning_rate
      else:
          learning_rate = cosine_decay(
              scaled_lr,
              global_step - warmup_steps,
              train_steps - warmup_steps,
          )
      return learning_rate
  
imports: import argparse
  import os
  import math
  import matplotlib.pyplot
  import torch
  import torch.nn
  from torchdiffeq import odeint, odeint_event
  from bouncing_ball import BouncingBallExample
label_desc: Use image node_id 3 for calling a global function with example usage: learning_rate_schedule(global_step, warmup_steps, base_learning_rate, lr_scaling, train_steps) and returns: learning_rate