class_docstr: Megatron Retrieval enhanced language model
class_docstr_tok: ["Megatron","Retrieval","enhanced","language","model"]
code: def predict_step(
      self,
      batch: Any,
      batch_idx: int,
      dataloader_idx: Optional[int] = None,
  ) -> Any:
      inference_config = self._inference_config
      if inference_config is None:
          return None
      else:
          # need to overwrite some configuration, make it immutable
          inference_config = inference_config.copy()
          compute_logprob = inference_config["compute_logprob"]
          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,
                  strategy=self.inference_strategy,
              )
              compute_prob_response = get_computeprob_response(
                  self.tokenizer, response, batch
              )
              return compute_prob_response
          else:
              inference_config["inputs"] = batch
              return generate(
                  self,
                  **inference_config,
                  strategy=self.inference_strategy,
              )
  
imports: import math
  from typing import Any, List, Optional, Union
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler
  from nemo.collections.nlp.data.language_modeling.megatron.retro_dataset import build_mock_train_valid_test_datasets, build_train_valid_test_datasets
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.modules.common.megatron.mup.init import normal_
  from nemo.collections.nlp.modules.common.megatron.mup.shape import set_base_shapes
  from nemo.collections.nlp.modules.common.megatron.retrieval_token_level_encoder_decoder import MegatronRetrievalTokenLevelEncoderDecoderModule
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group, build_position_ids, get_params_for_weight_decay_optimization
  from nemo.collections.nlp.modules.common.text_generation_strategy import model_inference_strategy_dispatcher
  from nemo.collections.nlp.modules.common.text_generation_utils import generate, get_computeprob_response, get_default_length_params, get_default_sampling_params, megatron_gpt_generate
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
  from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, OutputType, SamplingParam, TextGeneration
  from nemo.collections.nlp.parts.nlp_overrides import GradScaler
  from nemo.utils import AppState, logging
label_desc: Use image node_id 14 for calling the MegatronRetrievalModel obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: None, compute_prob_response, generate