class_docstr: The trainer with a supervised learning loss. Supports Cross-Entropy, MSE and MAE losses.
  
  Args:
      policy: The policy to be trained.
code: def configure_optimizers(self) -> torch.optim.Optimizer:
      return torch.optim.Adam(
          self.parameters(), lr=self.lr, weight_decay=self.weight_decay
      )
  
file_path: ReAgent/reagent/training/cb/supervised_trainer.py
imports: import logging
  import torch
  from reagent.core.types import CBInput
  from reagent.gym.policies.policy import Policy
  from reagent.training.cb.base_trainer import BaseCBTrainerWithEval
label_desc: Use image node_id 2 for calling the SupervisedTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: torch