code: def read_metadata(tracker_filename):
      # Read the tracker file and either set the iteration or
      # mark it as a release checkpoint.
      iteration = 0
      release = False
      with open(tracker_filename, "r") as f:
          metastring = f.read().strip()
          try:
              iteration = int(metastring)
          except ValueError:
              release = metastring == "release"
              if not release:
                  print_rank_0(
                      "ERROR: Invalid metadata file {}. Exiting".format(
                          tracker_filename
                      )
                  )
                  sys.exit()
      assert (
          iteration > 0 or release
      ), "error parsing metadata file {}".format(tracker_filename)
  
      # Get the max iteration retrieved across the ranks.
      if torch.distributed.is_initialized():
          iters_cuda = torch.tensor(
              [iteration], dtype=torch.long, device="cuda"
          )
          torch.distributed.all_reduce(
              iters_cuda, op=torch.distributed.ReduceOp.MAX
          )
          max_iter = iters_cuda[0].item()
  
          # We should now have all the same iteration.
          # If not, print a warning and chose the maximum
          # iteration across all ranks.
          if iteration != max_iter:
              rank = torch.distributed.get_rank()
              print(
                  "WARNING: on rank {} found iteration {} in the "
                  "metadata while max iteration across the ranks "
                  "is {}, replacing it with max iteration.".format(
                      rank, iteration, max_iter
                  ),
                  flush=True,
              )
      else:
          # When loading a checkpoint outside of training (for example,
          # when editing it), we might not have torch distributed
          # initialized, in this case, just assume we have the latest
          max_iter = iteration
      return max_iter, release
  
imports: import os
  import random
  import sys
  import numpy
  import torch
  from megatron import update_num_microbatches
  from megatron.core import mpu, tensor_parallel
  from .global_vars import get_args
  from .utils import unwrap_model, print_rank_0
label_desc: Use image node_id 9 for calling a global function with example usage: read_metadata(tracker_filename) and returns: max_iter,  release