class_docstr: Megatron GPT Supervised Fine-Tuning
class_docstr_tok: ["Megatron","GPT","Supervised","Fine-Tuning"]
code: def _reconfigure_and_process_inference_batch(self, batch, data_cfg):
      global_batch_size_per_gpu = batch["tokens"].size(0)
      # This should happen only on the last batch of the dataset.
      if (
          global_batch_size_per_gpu
          != get_current_global_batch_size()
          // parallel_state.get_data_parallel_world_size()
      ):
          # NOTE: This is reconfiguring to make sure there is no grad-acc for validation batches.
          if (
              global_batch_size_per_gpu
              != data_cfg.global_batch_size
              // parallel_state.get_data_parallel_world_size()
          ):
              app_state = AppState()
              _reconfigure_microbatch_calculator(
                  rank=app_state.global_rank,
                  rampup_batch_size=None,
                  global_batch_size=global_batch_size_per_gpu
                  * parallel_state.get_data_parallel_world_size(),
                  micro_batch_size=global_batch_size_per_gpu,
                  data_parallel_size=parallel_state.get_data_parallel_world_size(),
              )
          # NOTE: need to explicitly handle resetting for multi-validation
          else:
              app_state = AppState()
              _reconfigure_microbatch_calculator(
                  rank=app_state.global_rank,
                  rampup_batch_size=None,
                  global_batch_size=data_cfg.global_batch_size,
                  micro_batch_size=data_cfg.micro_batch_size,
                  data_parallel_size=parallel_state.get_data_parallel_world_size(),
              )
  
imports: import itertools
  import json
  from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.metrics import MetricStringToTorchMetric
  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.gpt_sft_chat_dataset import GPTSFTChatDataset
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_dataset import GPTSFTDataset, GPTSFTPackedDataset
  from nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers import MegatronPretrainingBatchSampler
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.modules.common.megatron.utils import get_iterator_k_split
  from nemo.collections.nlp.modules.common.text_generation_utils import generate, get_computeprob_response
  from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 15 for calling the MegatronGPTSFTModel obj's underlying member method code with example usage: obj._reconfigure_and_process_inference_batch(batch, data_cfg) without return types