class_docstr: GPT Actor model.
  
  Args:
      pretrained (str): Pretrained model name or path.
      config (GPT2Config): Model config.
      checkpoint (bool): Enable gradient checkpointing.
      lora_rank (int): Rank of the LoRa layer.
      lora_train_bias (str): Bias training strategy for the LoRa layer.
code: def __init__(
      self,
      pretrained: Optional[str] = None,
      config: Optional[GPT2Config] = None,
      checkpoint: bool = False,
      lora_rank: int = 0,
      lora_train_bias: str = "none",
      **kwargs,
  ) -> None:
      if pretrained is not None:
          model = GPT2LMHeadModel.from_pretrained(pretrained)
      elif config is not None:
          model = GPT2LMHeadModel(config)
      else:
          model = GPT2LMHeadModel(GPT2Config())
      if checkpoint:
          model.gradient_checkpointing_enable()
      super().__init__(model, lora_rank, lora_train_bias, **kwargs)
  
imports: from typing import Optional
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
  from ..base import Actor
label_desc: Use image node_id 1 to create a new GPTActor object from inherited base classes: Actor with example: obj = GPTActor(pretrained, config, checkpoint, lora_rank, lora_train_bias)