code: def get_parametric_input(max_num_actions: int, obs: rlt.FeatureData):
      assert (
          len(obs.float_features.shape) == 2
      ), f"{obs.float_features.shape} is not (batch_size, state_dim)."
      batch_size, _ = obs.float_features.shape
      possible_actions = get_possible_actions_for_gym(
          batch_size, max_num_actions
      ).to(obs.float_features.device)
      return obs.get_tiled_batch(max_num_actions), possible_actions
  
file_path: ReAgent/reagent/gym/policies/scorers/discrete_scorer.py
imports: from typing import Optional, Tuple
  import reagent.core.types
  import torch
  from reagent.gym.preprocessors.trainer_preprocessor import get_possible_actions_for_gym
  from reagent.gym.types import Scorer
  from reagent.models.base import ModelBase
label_desc: Use image node_id 4 for calling a global function with example usage: get_parametric_input(max_num_actions, obs) and returns: obs,  possible_actions