code: def __init__(
      self,
      model,
      head_name,
      vocab_size=None,
      embedding_size=None,
      layers=1,
      activation_function=None,
      layer_norm=False,
      bias=False,
      shift_labels=True,
      dropout_prob=None,
  ):
      super(CausalLMHead, self).__init__(head_name)
      self.config = {
          "head_type": "causal_lm",
          "vocab_size": vocab_size or model.config.vocab_size,
          "embedding_size": embedding_size
          or getattr(
              model.config, "embedding_size", model.config.hidden_size
          ),
          "layers": layers,
          "activation_function": activation_function,
          "layer_norm": layer_norm,
          "bias": bias,
          "shift_labels": shift_labels,
          "label2id": None,
          "dropout_prob": dropout_prob,
      }
      self.build(model)
  
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 1 to create a new CausalLMHead object from inherited base classes: PredictionHead with example: obj = CausalLMHead(model, head_name, vocab_size, embedding_size, layers, activation_function, layer_norm, bias, shift_labels, dropout_prob)