code: def evaluate(
      forward_step_func,
      data_iterator,
      model,
      process_non_loss_data_func,
      config,
      verbose=False,
  ):
      """Evaluation."""
      args = get_args()
      timers = get_timers()
  
      timers("evaluate", log_level=0).start(barrier=True)
  
      if (
          args.vision_pretraining
          and args.vision_pretraining_type == "dino"
      ):
          compute_feature_bank(model)
  
      # Turn on evaluation mode which disables dropout.
      for model_module in model:
          model_module.eval()
  
      total_loss_dict = {}
  
      # make validation batch size independent from training batch size
      eval_batch_size = args.global_batch_size
      eval_num_microbatches = eval_batch_size // (
          args.micro_batch_size * args.data_parallel_size
      )
  
      with torch.no_grad():
          iteration = 0
          if verbose:
              print_rank_0(
                  f"Evaluating on {args.eval_iters * eval_batch_size} samples"
              )
          while iteration < args.eval_iters:
              iteration += 1
              if verbose:
                  print_rank_0(
                      f"Evaluating iter {iteration}/{args.eval_iters}"
                  )
  
              forward_backward_func = get_forward_backward_func()
              # Don't care about timing during evaluation
              config.timers = None
              loss_dicts = forward_backward_func(
                  forward_step_func=forward_step_func,
                  data_iterator=data_iterator,
                  model=model,
                  num_microbatches=eval_num_microbatches,
                  seq_length=args.seq_length,
                  micro_batch_size=args.micro_batch_size,
                  decoder_seq_length=args.decoder_seq_length,
                  forward_only=True,
              )
              config.timers = get_timers()
  
              # Empty unused memory
              if args.empty_unused_memory_level >= 1:
                  torch.cuda.empty_cache()
  
              if mpu.is_pipeline_last_stage(ignore_virtual=True):
                  # Reduce across processes.
                  for loss_dict in loss_dicts:
                      for key in loss_dict:
                          total_loss_dict[key] = (
                              total_loss_dict.get(
                                  key,
                                  torch.tensor(
                                      [0.0],
                                      dtype=torch.float,
                                      device="cuda",
                                  ),
                              )
                              + loss_dict[key]
                          )
  
              args.consumed_valid_samples += eval_batch_size
  
              if args.exit_duration_in_mins:
                  train_time = (time.time() - _TRAIN_START_TIME) / 60.0
                  done_cuda = torch.tensor(
                      [train_time > args.exit_duration_in_mins],
                      dtype=torch.int,
                      device="cuda",
                  )
                  torch.distributed.all_reduce(
                      done_cuda, op=torch.distributed.ReduceOp.MAX
                  )
                  done = done_cuda.item()
                  if done:
                      print_rank_0(
                          "Exiting during evaluation, timelimit reached"
                      )
                      return None, None, True
  
          collected_non_loss_data = None
          if process_non_loss_data_func is not None and is_last_rank():
              collected_non_loss_data = forward_backward_func(
                  forward_step_func=forward_step_func,
                  data_iterator=data_iterator,
                  model=model,
                  num_microbatches=get_num_microbatches(),
                  seq_length=args.seq_length,
                  micro_batch_size=args.micro_batch_size,
                  decoder_seq_length=args.decoder_seq_length,
                  forward_only=True,
                  collect_non_loss_data=True,
              )
  
      # Move model back to the train mode.
      for model_module in model:
          model_module.train()
  
      for key in total_loss_dict:
          total_loss_dict[key] /= (
              args.eval_iters * eval_num_microbatches
          )
  
      timers("evaluate").stop()
      timers.log(["evaluate"])
  
      return total_loss_dict, collected_non_loss_data, False
  
imports: import gc
  from datetime import datetime
  import math
  import logging
  import sys
  from .log_handler import CustomHandler
  from .theoretical_memory_usage import report_theoretical_memory
  import time
  import torch
  from megatron import get_args
  from megatron import get_signal_handler
  from megatron import get_timers
  from megatron import get_tensorboard_writer
  from megatron import get_wandb_writer
  from megatron import get_current_global_batch_size
  from megatron import get_num_microbatches
  from megatron import is_last_rank
  from megatron import update_num_microbatches
  from megatron.core import mpu, tensor_parallel
  from megatron.core.utils import get_model_config
  from megatron import print_rank_0
  from megatron import print_rank_last
  from megatron.checkpointing import load_checkpoint
  from megatron.checkpointing import save_checkpoint
  from megatron.model import Float16Module
  from megatron.model import GPTModel
  from megatron.core.distributed import DistributedDataParallel
  from megatron.core.distributed import finalize_model_grads
  from megatron.core.enums import ModelType
  from megatron.optimizer import get_megatron_optimizer
  from megatron.initialize import initialize_megatron
  from megatron.initialize import write_args_to_tensorboard
  from megatron.initialize import set_jit_fusion_options
  from megatron.optimizer_param_scheduler import OptimizerParamScheduler
  from megatron.utils import check_adlr_autoresume_termination
  from megatron.utils import unwrap_model
  from megatron.data.data_samplers import build_pretraining_data_loader
  from megatron.utils import calc_params_l2_norm
  from megatron.core.pipeline_parallel import get_forward_backward_func
  from megatron.utils import report_memory
  from megatron.model.vision.knn_monitor import compute_feature_bank
label_desc: Use image node_id 12 for calling a global function with example usage: evaluate(forward_step_func, data_iterator, model, process_non_loss_data_func, config, verbose) and returns: total_loss_dict,  collected_non_loss_data,  False, None,  None,  True