class_docstr: MegatronGPTPTuningModel is a model that combines a base model (GPTSFTModel) with a p-tuning prefix in the
  input word embedding representations using a prompt-encoder as descripted in Liu et al. https://arxiv.org/pdf/2103.10385.pdf
  
  The mixin framework adds the output of prompt-encoder (i.e. the virtual embeddings) inside
  nemo/collections/nlp/modules/common/megatron/language_model.py
code: def setup_optimizer_param_groups(self):
      if self.first_stage_of_pipeline():
          super().setup_optimizer_param_groups()
      else:
          self.freeze()  # Freeze the entire model
          self._optimizer_param_groups = ({"params": []},)
      logging.info(f"Optimizer groups set:\n{self.summarize()}")
  
imports: from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
  from nemo.collections.nlp.modules.common.megatron.adapters.mcore_mixins import MCoreGPTEmbeddingMixin, MCoreSelfAttentionMixin, MCoreTransformerLayerMixin, swap_mcore_mixin
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, InfusedAdapterConfig, LoraKQVAdapterConfig, LoraKQVAdapterWeightTyingConfig, MLPInfusedAdapterConfig, ParallelLinearAdapterConfig, ParallelLinearAdapterWeightTyingConfig, PromptEncoderAdapterConfig
  from nemo.core.classes.mixins import adapter_mixins
  from nemo.utils import logging, model_utils
  from nemo.utils.decorators import deprecated
label_desc: Use image node_id 6 for calling the MegatronGPTPTuningModel obj's underlying member method code with example usage: obj.setup_optimizer_param_groups() without return types