class_docstr: Model to be trained to predict the reward for RL.
  or to be used as Critic in RL. It is a Language Model with a head
  that predicts the reward (a scalar) for a given sequence of tokens.
  
  Attributes:
      model (torch.nn.Module): Model to be used for the reward model
      tokenizer (torch.nn.Module): Tokenizer to be used for the reward model
      head (torch.nn.Module): Head to be used for the reward model
      config (ConfigReward): Config parameters for the reward model
  
  Methods:
      load_tokenizer: Load the tokenizer for the reward model
      forward: Forward pass of the model (used by the critic)
      save: Save the model
      load: Load the model
      get_reward: Get the reward for a given input (used by the reward model)
      parameters: Return the parameters of the reward model
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docstr: Get the reward for the given output sequence
  
  Args:
      output_sequence (torch.Tensor): The concatenation of initial input
          and actor output as tokens
      output_sequence_mask (torch.Tensor): Mask for the attention
docstr_tok: ["Get","the","reward","for","the","given","output","sequence","Args",":","output_sequence","(","torch.Tensor",")",":","The","concatenation","of","initial","input","and","actor","output","as","tokens","output_sequence_mask","(","torch.Tensor",")",":","Mask","for","the","attention"]
code: def get_reward(
      self,
      output_sequence: torch.Tensor,
      output_sequence_mask: torch.Tensor,
  ) -> torch.Tensor:
      """Get the reward for the given output sequence
  
      Args:
          output_sequence (torch.Tensor): The concatenation of initial input
              and actor output as tokens
          output_sequence_mask (torch.Tensor): Mask for the attention
      """
      if output_sequence.shape[1] > self.config.max_sequence_length:
          raise ValueError(
              f"Output sequence is too long: {output_sequence.shape[1]}"
              f" > {self.config.max_sequence_length}"
          )
      rewards = self.forward(output_sequence, output_sequence_mask)
      return rewards[:, -1]
  
imports: import json
  import shutil
  import os
  import deepspeed
  import torch
  from accelerate import Accelerator
  from beartype import beartype
  from beartype.typing import Iterable, Tuple
  from einops.layers.torch import Rearrange
  from torch.utils.data import Dataset, DataLoader
  from transformers import AutoModel, AutoTokenizer
  from chatllama.rlhf.config import ConfigReward
  from chatllama.rlhf.model_list import hf_models
  from chatllama.rlhf.model_loader import ModelLoader
  from chatllama.rlhf.utils import TrainingStats
label_desc: Use image node_id 7 for calling the RewardModel obj's underlying member method code with example usage: obj.get_reward(output_sequence, output_sequence_mask) and returns: rewards