code: def build(self, model):
      model_config = model.config
      # Additional FC layers
      pred_head = []
      with_layer_norm = self.config.get("layer_norm", False)
      embedding_size = self.config.get(
          "embedding_size", model_config.hidden_size
      )
  
      for l_id in range(self.config["layers"] - 1):
          if l_id == 0:
              pred_head.append(
                  nn.Linear(model_config.hidden_size, embedding_size)
              )
          else:
              pred_head.append(
                  nn.Linear(embedding_size, embedding_size)
              )
          if self.config["activation_function"]:
              pred_head.append(
                  Activation_Function_Class(
                      self.config["activation_function"]
                  )
              )
          if with_layer_norm:
              eps = getattr(model_config, "layer_norm_eps", 1e-12)
              pred_head.append(nn.LayerNorm(embedding_size, eps=eps))
  
      for i, module in enumerate(pred_head):
          self.add_module(str(i), module)
  
      # Final embedding layer
      self.add_module(
          str(len(pred_head)),
          nn.Linear(
              embedding_size,
              self.config["vocab_size"],
              bias=self.config["bias"],
          ),
      )
  
      self.apply(model._init_weights)
      self.train(model.training)
  
imports: import torch
  import torch.nn
  from transformers.modeling_outputs import CausalLMOutput, CausalLMOutputWithPast, MaskedLMOutput, Seq2SeqLMOutput
  from ..methods.modeling import Activation_Function_Class
  from .base import PredictionHead
label_desc: Use image node_id 2 for calling the CausalLMHead obj's underlying member method code with example usage: obj.build(model) without return types