class_docstr: T0 (https://arxiv.org/abs/2110.08207) Model that Inherits from MegatronT5SFTModel and overrides the dataset building.
code: def _reconfigure_and_process_inference_batch(self, batch):
      global_batch_per_gpu = batch["text_enc"].size(0)
      # This should happen only on the last batch of the validation/test dataset with drop_last=False.
      if (
          global_batch_per_gpu
          != self.cfg.data.validation_ds.global_batch_size
      ):
          app_state = AppState()
          _reconfigure_microbatch_calculator(
              rank=app_state.global_rank,
              rampup_batch_size=None,
              global_batch_size=global_batch_per_gpu
              * parallel_state.get_data_parallel_world_size(),
              micro_batch_size=global_batch_per_gpu,
              data_parallel_size=parallel_state.get_data_parallel_world_size(),
          )
      return batch
  
imports: import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import get_datasets_weights_and_num_samples
  from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset
  from nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers import MegatronPretrainingBatchSampler
  from nemo.collections.nlp.data.language_modeling.t0_dataset import T0Dataset
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.utils import AppState, logging
label_desc: Use image node_id 5 for calling the MegatronT0Model obj's underlying member method code with example usage: obj._reconfigure_and_process_inference_batch(batch) and returns: batch