code: def __init__(self, cfg: DictConfig, trainer: Trainer):
      super().__init__(cfg, trainer)
      save_restore_connector = NLPSaveRestoreConnector()
      if os.path.isdir(cfg.get("language_model_path")):
          save_restore_connector.model_extracted_dir = cfg.get(
              "language_model_path"
          )
      self.frozen_model_cfg = MegatronGPTModel.restore_from(
          cfg.get("language_model_path"),
          trainer=trainer,
          return_config=True,
          save_restore_connector=save_restore_connector,
      )
      self.adapter_name_keys = []
  
imports: import itertools
  import os
  import torch
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
  from nemo.collections.nlp.modules.common import VirtualPromptStyle
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, InfusedAdapterConfig, MLPInfusedAdapterConfig, ParallelLinearAdapterConfig
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.mixins import adapter_mixins
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 1 to create a new MegatronGPTBaseAdapterModel object from inherited base classes: MegatronGPTPromptLearningModel with example: obj = MegatronGPTBaseAdapterModel(cfg, trainer)