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
docstr: LightningModule hook:
  https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-load-checkpoint
docstr_tok: ["LightningModule","hook",":","https",":","\/\/pytorch-lightning.readthedocs.io\/en\/stable\/common\/lightning_module.html","#","on-load-checkpoint"]
code: def on_load_checkpoint(self, checkpoint) -> None:
      """LightningModule hook:
      https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-load-checkpoint
      """
      if self.setup_complete:
          if self.first_stage_of_pipeline():
              super().on_load_checkpoint(checkpoint)
      else:
          super().on_load_checkpoint(checkpoint)
  
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 5 for calling the MegatronGPTPTuningModel obj's underlying member method code with example usage: obj.on_load_checkpoint(checkpoint) without return types