docstr: Calculate validation metrics for a batch and return them as a dictionary.
  This method is not necessary if the user defines evaluate_full_dataset().
docstr_tok: ["Calculate","validation","metrics","for","a","batch","and","return","them","as","a","dictionary",".","This","method","is","not","necessary","if","the","user","defines","evaluate_full_dataset","(",")","."]
code: def evaluate_batch(self, data_iterator, batch_idx):
      """
      Calculate validation metrics for a batch and return them as a dictionary.
      This method is not necessary if the user defines evaluate_full_dataset().
      """
      if self.args.search_world_size:
          if self.tflops > 0:
              self.reported_flops = True
          return {"tflops": self.tflops}
  
      if data_iterator is not None:
          if self.neox_args.char_level_ppl:
              data_iterator = megatron_utils.CharCounter(
                  data_iterator, self.neox_args.tokenizer
              )
  
      loss = megatron_train.forward_step(
          model=self.model,
          data_iterator=data_iterator,
          neox_args=self.neox_args,
          timers=self.timers,
      )
  
      if data_iterator is not None:
          if self.neox_args.char_level_ppl:
              self.reducer.update(
                  loss.item(),
                  data_iterator.token_count,
                  data_iterator.char_count,
              )
          else:
              self.reducer.update(loss.item())
  
      if (
          self.neox_args.deepspeed
          and self.neox_args.checkpoint_activations
      ):
          deepspeed.checkpointing.reset()
  
      return {"lm_loss": loss}
  
imports: import logging
  import pathlib
  import traceback
  from datetime import datetime
  import deepspeed
  import megatron.training
  import megatron.utils
  import torch
  from attrdict import AttrMap
  from det_utils import EarlyStoppingCallback, EvalHarness, LMReducers, TensorboardWriter, get_neox_args
  from megatron import mpu
  from megatron.checkpointing import load_checkpoint, save_checkpoint
  from megatron.data.data_utils import build_datasets_from_neox_args
  from determined import LOG_FORMAT, InvalidHP
  from determined.pytorch import DataLoader
  from determined.pytorch.deepspeed import DeepSpeedTrial, DeepSpeedTrialContext, ModelParallelUnit
label_desc: Use image node_id 5 for calling the GPT2Trial obj's underlying member method code with example usage: obj.evaluate_batch(data_iterator, batch_idx) and returns: dict, dict