code: def log_run_lambda(aml_ctx, fold, model, nn, epoch, metrics):
      """A utility function that can be used with partial(), and can be serialized through multiprocessing."""
      log_run(aml_ctx, fold, model, epoch, metrics)
  
imports: import random
  import torch
  import torch.distributed
  from docopt import docopt
  from dpu_utils.utils import RichPath, run_and_debug
  from functools import partial
  from pathlib import Path
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from typing import Optional
  from ptgnn.baseneuralmodel.distributedtrainer import DistributedModelTrainer
  from ptgnn.baseneuralmodel.utils.amlutils import configure_logging, log_run
  from ptgnn.baseneuralmodel.utils.data import ShardedLazyDataIterable
  from ptgnn.implementations.typilus.graph2class import Graph2Class
  from ptgnn.implementations.typilus.train import create_graph2class_gnn_model
label_desc: Use image node_id 3 for calling a global function with example usage: log_run_lambda(aml_ctx, fold, model, nn, epoch, metrics) without return types