class_docstr: ZeroRedundancyOptimizer has a different interface than other optimizer and the `consolidate_state_dict`
  needs to be invoked.
  
  This class wraps `ZeroRedundancyOptimizer` and ensure that `state_dict()` works as all other optimizers
  (at least for rank 0).
class_docstr_tok: ["ZeroRedundancyOptimizer","has","a","different","interface","than","other","optimizer","and","the","`","consolidate_state_dict","`","needs","to","be","invoked",".","This","class","wraps","`","ZeroRedundancyOptimizer","`","and","ensure","that","`","state_dict","(",")","`","works","as","all","other","optimizers","(","at","least","for","rank","0",")","."]
code: def state_dict(self):
      self.consolidate_state_dict()
      if dist.get_rank() == 0:
          return ZeroRedundancyOptimizer.state_dict(self)
      return None
  
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 1 for calling the ZeroRedundancyOptimizer_ obj's underlying member method code with example usage: obj.state_dict() and returns: None, ZeroRedundancyOptimizer