class_docstr: Trainer for behavioral cloning learning.
  
  Runs training on top of provided trajectory data and rolls out the policy using the provided evaluator.
  
  In structured (multi-step) envs, all policies are trained simultaneously based on the substep actions
  and observation present in the trajectory data.
class_docstr_tok: ["Trainer","for","behavioral","cloning","learning",".","Runs","training","on","top","of","provided","trajectory","data","and","rolls","out","the","policy","using","the","provided","evaluator",".","In","structured","(","multi-step",")","envs",",","all","policies","are","trained","simultaneously","based","on","the","substep","actions","and","observation","present","in","the","trajectory","data","."]
docstr: Set the model and optimizer state.
  :param state_dict: The state dict.
docstr_tok: ["Set","the","model","and","optimizer","state",".",":","param","state_dict",":","The","state","dict","."]
code: def load_state_dict(self, state_dict: Dict) -> None:
      """Set the model and optimizer state.
      :param state_dict: The state dict.
      """
      self.policy.load_state_dict(state_dict)
  
imports: from dataclasses import dataclass
  from typing import Dict, Union, Optional, List
  import torch
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from typing.io import BinaryIO
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.annotations import override
  from maze.core.env.action_conversion import ActionType, TorchActionType
  from maze.core.env.observation_conversion import ObservationType, TorchObservationType
  from maze.core.env.structured_env import ActorID
  from maze.core.log_stats.log_stats import LogStatsAggregator, LogStatsLevel, get_stats_logger, increment_log_step
  from maze.perception.perception_utils import convert_to_torch
  from maze.train.trainers.common.evaluators.evaluator import Evaluator
  from maze.train.trainers.common.trainer import Trainer
  from maze.train.trainers.imitation.bc_algorithm_config import BCAlgorithmConfig
  from maze.train.trainers.imitation.bc_loss import BCLoss
  from maze.train.trainers.imitation.imitation_events import ImitationEvents
  from maze.train.utils.train_utils import compute_gradient_norm, debatch_actor_ids
label_desc: Use image node_id 3 for calling the BCTrainer obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types