class_docstr: Trainer for fitting Seq2Reward planning outcomes to a neural network-based policy
class_docstr_tok: ["Trainer","for","fitting","Seq2Reward","planning","outcomes","to","a","neural","network-based","policy"]
code: def warm_start_components(self):
      logger.info("No warm start components yet...")
      components = []
      return components
  
imports: import logging
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import Seq2RewardTrainerParameters
  from reagent.core.types import FeatureData
  from reagent.models.fully_connected_network import FloatFeatureFullyConnected
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import gen_permutations
  from reagent.training.world_model.seq2reward_trainer import get_Q
label_desc: Use image node_id 7 for calling the CompressModelTrainer obj's underlying member method code with example usage: obj.warm_start_components() and returns: components