code: def __init__(
      self,
      base_model,
      tokenizer,
      num_padding_at_beginning=0,
      compute_fp32_loss=False,
  ):
      super().__init__()
      self.config = base_model.config
      self.num_padding_at_beginning = num_padding_at_beginning
      if hasattr(self.config, "word_embed_proj_dim"):
          # `OPT` models use word_embed_proj_dim as final output
          # https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py#L497
          self.v_head = nn.Linear(
              self.config.word_embed_proj_dim, 1, bias=False
          )
      else:
          # `gpt-neo(x)` models use `hidden_size` attribute names instead of `n_embd``
          self.config.n_embd = (
              self.config.hidden_size
              if hasattr(self.config, "hidden_size")
              else self.config.n_embd
          )
          self.v_head = nn.Linear(self.config.n_embd, 1, bias=False)
      self.rwtransformer = base_model
      self.PAD_ID = tokenizer.pad_token_id
      self.compute_fp32_loss = compute_fp32_loss
  
file_path: deepspeedexamples/applications/DeepSpeed-Chat/dschat/utils/model/reward_model.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new RewardModel object from inherited base classes: nn with example: obj = RewardModel(base_model, tokenizer, num_padding_at_beginning, compute_fp32_loss)