code: def get_possible_actions_for_gym(
      batch_size: int, num_actions: int
  ) -> rlt.FeatureData:
      """
      tiled_actions should be (batch_size * num_actions, num_actions)
      forall i in [batch_size],
      tiled_actions[i*num_actions:(i+1)*num_actions] should be I[num_actions]
      where I[n] is the n-dimensional identity matrix.
      NOTE: this is only the case for when we convert discrete action to
      parametric action via one-hot encoding.
      """
      possible_actions = torch.eye(num_actions).repeat(
          repeats=(batch_size, 1)
      )
      return rlt.FeatureData(float_features=possible_actions)
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 5 for calling a global function with example usage: get_possible_actions_for_gym(batch_size, num_actions) and returns: rlt