code: def _linear_warmup_lr_lambda(
      current_step: int, num_warmup_steps: int, num_training_steps: int
  ):
      if current_step < num_warmup_steps:
          return float(current_step) / float(max(1, num_warmup_steps))
      return max(
          0.0,
          float(num_training_steps - current_step)
          / float(max(1, num_training_steps - num_warmup_steps)),
      )
  
file_path: autogluon/multimodal/src/autogluon/multimodal/optimization/lr_scheduler.py
imports: import functools
  import math
  from torch.optim.lr_scheduler import LambdaLR
label_desc: Use image node_id 5 for calling a global function with example usage: _linear_warmup_lr_lambda(current_step, num_warmup_steps, num_training_steps) and returns: max, unknown