code: def configure_optimizers(self):
      if not self.train_from_scratch:
          # Freeze those weights
          for p in self.model.base_model.parameters():
              p.requires_grad = False
          self._fine_tune_layers()
      params_to_update = filter(
          lambda p: p.requires_grad, self.model.parameters()
      )
      return self.optimizer_fn(params_to_update, self.learning_rate)
  
file_path: SynapseML/deep-learning/src/main/python/synapse/ml/dl/LitDeepTextModel.py
imports: import inspect
  import pytorch_lightning
  import torch
  import torch.nn.functional
  import torch.optim
  from pytorch_lightning.utilities import _module_available
label_desc: Use image node_id 4 for calling the LitDeepTextModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: self