class_docstr: Model class for prompt-tuning or p-tuning a pretrained Megatron T5 model. 
  
  Prompt Tuning initalizes virtual prompt embeddings directly from a copy of
  certain token embeddings from the the pretrained T5 model's vocabulary
  and directly tunes these embedding weights. The token embeddings used in 
  initalization are specified by the user in the config file. The model can 
  be prompt-tuned for multiple tasks at once. Virtual prompts are stored in a 
  prompt table and can be added or deleted without disrupting virtual prompts 
  for other tasks. 
  
  P-tuning initializes an LSTM encoder model that generates virtual prompt
  embeddings for every task. Each task shares the same encoder. After p-tuning
  is compelete, the learned virtual prompts can be saved to the prompt table
  using add_ptuned_prompts_to_prompt_table(). Thus, if a user wants to add a 
  new virtual prompt via p-tuning, they do not need to retrain on all previous 
  tasks. This gives p-tuning the same task flexiblity as prompt-tuning.
code: def predict_step(
      self, batch: Any, batch_idx: int, dataloader_idx: int = 0
  ) -> Any:
      (
          input_ids,
          dec_input,
          labels,
          loss_mask,
          enc_mask,
          dec_mask,
          position_ids,
          taskname_ids,
      ) = batch
  
      batch_size, seq_length = input_ids.shape
      if self.first_stage_of_pipeline():
          input_embeds = self.embed_input(
              input_ids, taskname_ids, use_cached_reps=True
          )
  
          # TODO: This check needs to be revisited with PP support.
          if hasattr(
              self.frozen_model.enc_dec_model.encoder_embedding,
              "position_embeddings",
          ):
              position_embeddings = self.frozen_model.enc_dec_model.encoder_embedding.position_embeddings(
                  position_ids
              )
              encoder_input = input_embeds + position_embeddings
          else:
              encoder_input = input_embeds
  
      else:
          encoder_input = torch.zeros(
              (batch_size, seq_length, self.hidden_size),
              dtype=self.autocast_dtype,
          ).cuda()
  
      predicted_token_ids, log_probs = self.frozen_model.decode(
          tokens_enc=input_ids,
          enc_mask=enc_mask,
          num_tokens_to_generate=self.decoder_seq_length,
          encoder_input=encoder_input,
          bos_id=self.tokenizer.pad_id
          if self.cfg.data.get("decoder_starts_with_pad", False)
          else self.tokenizer.bos_id,
      )
      # Special ids to text function to handle stripping <eos> and special tokens with sentencepiece tokenizers.
      preds_text = MegatronT5SFTModel.ids_to_text(
          predicted_token_ids, self.tokenizer
      )
      input_text = MegatronT5SFTModel.ids_to_text(
          input_ids, self.tokenizer
      )
  
      if labels is not None:
          labels_text = MegatronT5SFTModel.ids_to_text(
              labels, self.tokenizer
          )
      else:
          labels_text = [None] * len(preds_text)
  
      return {
          "input_text": input_text,
          "preds_text": preds_text,
          "labels_text": labels_text,
      }
  
imports: import itertools
  from typing import Any, List
  import torch
  from omegaconf import OmegaConf
  from omegaconf.dictconfig import DictConfig
  from omegaconf.omegaconf import open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.t5_prompt_learning_dataset import T5PromptLearningDataset
  from nemo.collections.nlp.models.language_modeling.megatron_base_prompt_learning_model import MegatronBasePromptLearningModel
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group, get_iterator_k_split
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 19 for calling the MegatronT5PromptLearningModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: dict