code: def _load_base_checkpoint(load_dir, rank0=False):
      """Load the base state_dict from the given directory
  
      If rank0 is true, just loads rank 0 checkpoint, ignoring arguments.
      """
  
      # Read the tracker file and set the iteration.
      tracker_filename = get_checkpoint_tracker_filename(load_dir)
  
      # If no tracker file, return nothing
      if not os.path.isfile(tracker_filename):
          if not rank0:
              print_rank_0(
                  "WARNING: could not find the metadata file {} ".format(
                      tracker_filename
                  )
              )
              print_rank_0(
                  "    will not load any checkpoints and will start from "
                  "random"
              )
          return None, "", False
  
      # Otherwise, read the tracker file and either set the iteration or
      # mark it as a release checkpoint.
      iteration, release = read_metadata(tracker_filename)
  
      # Checkpoint.
      if rank0:
          checkpoint_name = find_checkpoint_rank_0(
              load_dir, iteration, release
          )
      else:
          checkpoint_name = get_checkpoint_name(
              load_dir, iteration, release
          )
          if release:
              print_rank_0(
                  f" loading release checkpoint from {load_dir}"
              )
          else:
              print_rank_0(
                  f" loading checkpoint from {load_dir} at iteration {iteration}"
              )
  
      # Load the checkpoint.
      try:
          state_dict = torch.load(checkpoint_name, map_location="cpu")
      except ModuleNotFoundError:
          from megatron.fp16_deprecated import loss_scaler
  
          # For backward compatibility.
          if not rank0:
              print_rank_0(
                  " > deserializing using the old code structure ..."
              )
          sys.modules["fp16.loss_scaler"] = sys.modules[
              "megatron.fp16_deprecated.loss_scaler"
          ]
          sys.modules["megatron.fp16.loss_scaler"] = sys.modules[
              "megatron.fp16_deprecated.loss_scaler"
          ]
          state_dict = torch.load(checkpoint_name, map_location="cpu")
          sys.modules.pop("fp16.loss_scaler", None)
          sys.modules.pop("megatron.fp16.loss_scaler", None)
      except BaseException as e:
          print_rank_0("could not load the checkpoint")
          print_rank_0(e)
          sys.exit()
  
      return state_dict, checkpoint_name, 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 14 for calling a global function with example usage: _load_base_checkpoint(load_dir, rank0) and returns: state_dict,  checkpoint_name,  release, None,  str,  False