code: def adam_weight_decay(optimizer):
      """
      Custom weight decay operation, not effecting grad values.
      """
      for group in optimizer.param_groups:
          for param in group["params"]:
              current_lr = group["lr"]
              weight_decay = group["weight_decay"]
              factor = -weight_decay * group["lr"]
              param.data = param.data.add(param.data, alpha=factor)
      return optimizer, current_lr
  
file_path: TTS/TTS/utils/training.py
imports: import torch
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: adam_weight_decay(optimizer) and returns: optimizer,  current_lr