code: def get_dummy_batch(action_type, num_batches):
      if action_type == "one-hot":
          action = torch.tensor(
              [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
          )
      else:
          raise TypeError("the actions (labels) should be one-hot")
  
      possible_actions_mask = torch.tensor(
          [
              [1, 1, 0, 0],
              [0, 1, 1, 0],
              [0, 0, 1, 1],
              [1, 0, 0, 1]
              # 1 means no mask. This mask keeps the label position (diagonal position) and some other position
          ]
      )
  
      batches = [None for _ in range(num_batches)]
      for i in range(num_batches):
          state = torch.tensor(
              [
                  [+0.1, +0.2, +0.3, +0.4, +0.5, +0.6, +0.7, +0.8],
                  [+0.1, +0.2, +0.3, +0.4, -0.5, -0.6, -0.7, -0.8],
                  [-0.1, -0.2, -0.3, -0.4, +0.5, +0.6, +0.7, +0.8],
                  [-0.1, -0.2, -0.3, -0.4, -0.5, -0.6, -0.7, -0.8],
              ]
          )
          # 8*1 float embedding
          # -------- means label=0
          # ----++++ means label=1
          # ++++---- means label=2
          # ++++++++ means label=3
          state = state + (1e-8**0.5) * torch.rand_like(
              state
          )  # add rand noise
          i_th_training_batch = rlt.BehavioralCloningModelInput(
              state=rlt.FeatureData(float_features=state),
              action=action,
              possible_actions_mask=possible_actions_mask,
          )
          batches[i] = i_th_training_batch
      return batches
  
imports: import logging
  import unittest
  from enum import Enum
  import pytorch_lightning
  import torch
  from pytorch_lightning import seed_everything
  from reagent.core import types
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.optimizer.union import classes, Optimizer__Union
  from reagent.training.behavioral_cloning_trainer import BehavioralCloningTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: get_dummy_batch(action_type, num_batches) and returns: batches