class_docstr: Megatron GPT Supervised Fine-Tuning
class_docstr_tok: ["Megatron","GPT","Supervised","Fine-Tuning"]
code: def predict_step(
      self,
      batch: Any,
      batch_idx: int,
      dataloader_idx: Optional[int] = None,
  ) -> Any:
      inference_config = self.get_inference_config()
      # need to overwrite some configuration, make it immutable
      inference_config = inference_config.copy()
      global_batch_size_per_gpu = batch["tokens"].size(0)
      num_micro_batches_before_decode = get_num_microbatches()
  
      compute_logprob = inference_config.get("compute_logprob", False)
      if compute_logprob:
          inference_config["inputs"] = batch
          inference_config["tokens_to_generate"] = 1
          inference_config["all_probs"] = True
          inference_config["add_BOS"] = False
          inference_config["greedy"] = True
          response = generate(self, **inference_config)
          response = get_computeprob_response(
              self.tokenizer, response, batch
          )
      else:
          # for megatron_gpt_eval.py
          if isinstance(batch, list):
              inference_config["inputs"] = batch
          else:
              # peft_eval.py
              inference_config["inputs"] = (
                  batch["contexts"].cuda(),
                  batch["context_lengths"].cuda(),
              )
          response = generate(self, **inference_config)
  
      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
          // num_micro_batches_before_decode,
          data_parallel_size=parallel_state.get_data_parallel_world_size(),
      )
  
      return response
  
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 12 for calling the MegatronGPTSFTModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: response