code: def _check_input(
      self, training_batch: rlt.BehavioralCloningModelInput
  ):
      assert isinstance(training_batch, rlt.BehavioralCloningModelInput)
      labels = training_batch.action
      if (
          len(labels.shape) > 1 and labels.shape[0] > 1
      ):  # check one hot label
          pass
      else:
          raise TypeError(
              "label tensor format or dimension does not match loss function"
          )
      assert torch.all(
          training_batch.action * training_batch.possible_actions_mask
          == training_batch.action
      )
  
file_path: ReAgent/reagent/training/behavioral_cloning_trainer.py
imports: import logging
  import reagent.core.types
  import torch
  from reagent.core.dataclasses import field
  from reagent.models.base import ModelBase
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 6 for calling the BehavioralCloningTrainer obj's underlying member method code with example usage: obj._check_input(training_batch) without return types