code: def predict_step(
      self, batch: Any, batch_idx: int, dataloader_idx: int = 0
  ) -> Any:
      (
          enc_input,
          dec_input,
          labels,
          loss_mask,
          enc_mask,
          dec_mask,
          position_ids,
          taskname_ids,
      ) = batch
      gbs = self.cfg.get(
          "validation_global_batch_size", self.cfg.global_batch_size
      )
      self._reconfigure_and_process_inference_batch(
          enc_input.size(0), gbs
      )
      predicted_token_ids, log_probs = self.frozen_model.decode(
          tokens_enc=enc_input,
          enc_mask=enc_mask,
          num_tokens_to_generate=self.decoder_seq_length,
          encoder_input=None,
      )
  
      # 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(
          enc_input, 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
  import torch
  from omegaconf.dictconfig import DictConfig
  from omegaconf.omegaconf import open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  from nemo.collections.nlp.models.language_modeling.megatron_t5_prompt_learning_model import MegatronT5PromptLearningModel
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.collections.nlp.modules.common import VirtualPromptStyle
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, InfusedAdapterConfig, LoraKQVAdapterConfig, LoraKVAdapterConfig, LoraQAdapterConfig, MLPInfusedAdapterConfig, ParallelLinearAdapterConfig
  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 7 for calling the MegatronT5BaseAdapterModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: dict