code: def training_log(
      loss_dict,
      total_loss_dict,
      learning_rate,
      iteration,
      loss_scale,
      report_memory_flag,
      skipped_iter,
      grad_norm,
      params_norm,
      num_zeros_in_grad,
  ):
      """Log training information such as losses, timing, ...."""
      args = get_args()
      timers = get_timers()
      writer = get_tensorboard_writer()
      wandb_writer = get_wandb_writer()
  
      # Advanced, skipped, and Nan iterations.
      advanced_iters_key = "advanced iterations"
      skipped_iters_key = "skipped iterations"
      nan_iters_key = "nan iterations"
      # Advanced iterations.
      if not skipped_iter:
          total_loss_dict[advanced_iters_key] = (
              total_loss_dict.get(advanced_iters_key, 0) + 1
          )
      else:
          if advanced_iters_key not in total_loss_dict:
              total_loss_dict[advanced_iters_key] = 0
      # Skipped iterations.
      total_loss_dict[skipped_iters_key] = (
          total_loss_dict.get(skipped_iters_key, 0) + skipped_iter
      )
      # Update losses and set nan iterations
      got_nan = False
      for key in loss_dict:
          if not skipped_iter:
              total_loss_dict[key] = (
                  total_loss_dict.get(
                      key,
                      torch.tensor(
                          [0.0], dtype=torch.float, device="cuda"
                      ),
                  )
                  + loss_dict[key]
              )
          else:
              value = loss_dict[key].float().sum().item()
              is_nan = (
                  value == float("inf")
                  or value == -float("inf")
                  or value != value
              )
              got_nan = got_nan or is_nan
      total_loss_dict[nan_iters_key] = total_loss_dict.get(
          nan_iters_key, 0
      ) + int(got_nan)
  
      # Logging.
      timers_to_log = [
          "forward-backward",
          "forward-compute",
          "backward-compute",
          "batch-generator",
          "forward-recv",
          "forward-send",
          "backward-recv",
          "backward-send",
          "forward-send-forward-recv",
          "forward-send-backward-recv",
          "backward-send-forward-recv",
          "backward-send-backward-recv",
          "forward-backward-send-forward-backward-recv",
          "layernorm-grads-all-reduce",
          "embedding-grads-all-reduce",
          "all-grads-sync",
          "params-all-gather",
          "optimizer-copy-to-main-grad",
          "optimizer-unscale-and-check-inf",
          "optimizer-clip-main-grad",
          "optimizer-count-zeros",
          "optimizer-inner-step",
          "optimizer-copy-main-to-model-params",
          "optimizer",
      ]
  
      # Calculate batch size.
      batch_size = (
          args.micro_batch_size
          * args.data_parallel_size
          * get_num_microbatches()
      )
  
      total_iterations = (
          total_loss_dict[advanced_iters_key]
          + total_loss_dict[skipped_iters_key]
      )
  
      # Tensorboard values.
      # Timer requires all the ranks to call.
      if args.log_timers_to_tensorboard and (
          iteration % args.tensorboard_log_interval == 0
      ):
          timers.write(
              timers_to_log,
              writer,
              iteration,
              normalizer=total_iterations,
          )
      if writer and (iteration % args.tensorboard_log_interval == 0):
          if wandb_writer:
              wandb_writer.log(
                  {"samples vs steps": args.consumed_train_samples},
                  iteration,
              )
          if args.log_learning_rate_to_tensorboard:
              writer.add_scalar(
                  "learning-rate", learning_rate, iteration
              )
              writer.add_scalar(
                  "learning-rate vs samples",
                  learning_rate,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"learning-rate": learning_rate}, iteration
                  )
          if args.log_batch_size_to_tensorboard:
              writer.add_scalar("batch-size", batch_size, iteration)
              writer.add_scalar(
                  "batch-size vs samples",
                  batch_size,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"batch-size": batch_size}, iteration
                  )
          for key in loss_dict:
              writer.add_scalar(key, loss_dict[key], iteration)
              writer.add_scalar(
                  key + " vs samples",
                  loss_dict[key],
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log({key: loss_dict[key]}, iteration)
          if args.log_loss_scale_to_tensorboard:
              writer.add_scalar("loss-scale", loss_scale, iteration)
              writer.add_scalar(
                  "loss-scale vs samples",
                  loss_scale,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"loss-scale": loss_scale}, iteration
                  )
          if args.log_world_size_to_tensorboard:
              writer.add_scalar(
                  "world-size", args.world_size, iteration
              )
              writer.add_scalar(
                  "world-size vs samples",
                  args.world_size,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"world-size": args.world_size}, iteration
                  )
          if grad_norm is not None:
              writer.add_scalar("grad-norm", grad_norm, iteration)
              writer.add_scalar(
                  "grad-norm vs samples",
                  grad_norm,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log({"grad-norm": grad_norm}, iteration)
          if num_zeros_in_grad is not None:
              writer.add_scalar(
                  "num-zeros", num_zeros_in_grad, iteration
              )
              writer.add_scalar(
                  "num-zeros vs samples",
                  num_zeros_in_grad,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"num-zeros": num_zeros_in_grad}, iteration
                  )
          if params_norm is not None:
              writer.add_scalar("params-norm", params_norm, iteration)
              writer.add_scalar(
                  "params-norm vs samples",
                  params_norm,
                  args.consumed_train_samples,
              )
              if wandb_writer:
                  wandb_writer.log(
                      {"params-norm": params_norm}, iteration
                  )
          if args.log_memory_to_tensorboard:
              mem_stats = torch.cuda.memory_stats()
              writer.add_scalar(
                  "mem-reserved-bytes",
                  mem_stats["reserved_bytes.all.current"],
                  iteration,
              )
              writer.add_scalar(
                  "mem-allocated-bytes",
                  mem_stats["allocated_bytes.all.current"],
                  iteration,
              )
              writer.add_scalar(
                  "mem-allocated-count",
                  mem_stats["allocation.all.current"],
                  iteration,
              )
  
      if iteration % args.log_interval == 0:
          elapsed_time = timers("interval-time").elapsed(barrier=True)
          elapsed_time_per_iteration = elapsed_time / total_iterations
          throughput = num_floating_point_operations(
              args, batch_size
          ) / (elapsed_time_per_iteration * 10**12 * args.world_size)
          if args.log_timers_to_tensorboard:
              if writer:
                  writer.add_scalar(
                      "iteration-time",
                      elapsed_time_per_iteration,
                      iteration,
                  )
              if wandb_writer:
                  wandb_writer.log(
                      {"iteration-time": elapsed_time_per_iteration},
                      iteration,
                  )
          log_string = " iteration {:8d}/{:8d} |".format(
              iteration, args.train_iters
          )
          log_string += " consumed samples: {:12d} |".format(
              args.consumed_train_samples
          )
          log_string += (
              " elapsed time per iteration (ms): {:.1f} |".format(
                  elapsed_time_per_iteration * 1000.0
              )
          )
          if args.log_throughput:
              log_string += f" throughput per GPU (TFLOP/s/GPU): {throughput:.1f} |"
              if args.log_timers_to_tensorboard:
                  if writer:
                      writer.add_scalar(
                          "throughput", throughput, iteration
                      )
                  if wandb_writer:
                      wandb_writer.log(
                          {"throughput": throughput}, iteration
                      )
          log_string += " learning rate: {:.3E} |".format(learning_rate)
          log_string += " global batch size: {:5d} |".format(batch_size)
          for key in total_loss_dict:
              if key not in [
                  advanced_iters_key,
                  skipped_iters_key,
                  nan_iters_key,
              ]:
                  avg = total_loss_dict[key].item() / float(
                      max(1, total_loss_dict[advanced_iters_key])
                  )
                  if avg > 0.0:
                      log_string += " {}: {:.6E} |".format(key, avg)
                  total_loss_dict[key] = torch.tensor(
                      [0.0], dtype=torch.float, device="cuda"
                  )
          log_string += " loss scale: {:.1f} |".format(loss_scale)
          if grad_norm is not None:
              log_string += " grad norm: {:.3f} |".format(grad_norm)
          if num_zeros_in_grad is not None:
              log_string += " num zeros: {:.1f} |".format(
                  num_zeros_in_grad
              )
          if params_norm is not None:
              log_string += " params norm: {:.3f} |".format(params_norm)
          log_string += " number of skipped iterations: {:3d} |".format(
              total_loss_dict[skipped_iters_key]
          )
          log_string += " number of nan iterations: {:3d} |".format(
              total_loss_dict[nan_iters_key]
          )
          total_loss_dict[advanced_iters_key] = 0
          total_loss_dict[skipped_iters_key] = 0
          total_loss_dict[nan_iters_key] = 0
          print_rank_last(log_string)
          if report_memory_flag and learning_rate > 0.0:
              # Report memory after optimizer state has been initialized.
              if torch.distributed.get_rank() == 0:
                  num_microbatches = get_num_microbatches()
                  report_theoretical_memory(
                      args,
                      num_microbatches=num_microbatches,
                      verbose=True,
                  )
              report_memory("(after {} iterations)".format(iteration))
              report_memory_flag = False
          timers.log(timers_to_log, normalizer=args.log_interval)
  
      return report_memory_flag
  
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 9 for calling a global function with example usage: training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag, skipped_iter, grad_norm, params_norm, num_zeros_in_grad) and returns: report_memory_flag