code: def _check_params(self):
      try:
          # TODO: Add other types of models here
          self.model = (
              AutoModelForSequenceClassification.from_pretrained(
                  self.checkpoint, num_labels=self.num_labels
              )
          )
          self._update_learning_rate()
      except Exception as err:
          raise ValueError(
              f"No checkpoint {self.checkpoint} found: {err=}, {type(err)=}"
          )
  
      if self.loss_name.lower() not in F.__dict__:
          raise ValueError(
              "No loss function: {} found".format(self.loss_name)
          )
      self.loss_fn = F.__dict__[self.loss_name.lower()]
  
      optimizers_mapping = {
          key.lower(): value
          for key, value in optim.__dict__.items()
          if inspect.isclass(value)
          and issubclass(value, optim.Optimizer)
      }
      if self.optimizer_name.lower() not in optimizers_mapping:
          raise ValueError(
              "No optimizer: {} found".format(self.optimizer_name)
          )
      self.optimizer_fn = optimizers_mapping[
          self.optimizer_name.lower()
      ]
  
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 2 for calling the LitDeepTextModel obj's underlying member method code with example usage: obj._check_params() without return types