class_docstr: Proposes uniform sharding plans, plans that have the same sharding type for all
  sharding options.
class_docstr_tok: ["Proposes","uniform","sharding","plans",",","plans","that","have","the","same","sharding","type","for","all","sharding","options","."]
code: def propose(self) -> Optional[List[ShardingOption]]:
      if self._proposal_index < len(self._grouped_sharding_options):
          return self._grouped_sharding_options[self._proposal_index]
      else:
          return None
  
imports: import copy
  import itertools
  import logging
  from decimal import Decimal
  from typing import cast, Dict, List, Optional, Set, Tuple, Union
  import torch
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.planner.types import Enumerator, Perf, Proposer, ShardingOption, Topology
  from torchrec.distributed.planner.utils import BinarySearchPredicate, bytes_to_gb, prod
label_desc: Use image node_id 4 for calling the UniformProposer obj's underlying member method code with example usage: obj.propose() and returns: self, None