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 __init__(self, use_depth: bool = True) -> None:
      self._use_depth: bool = use_depth
      self._grouped_sharding_options: List[List[ShardingOption]] = []
      self._proposal_index: int = 0
  
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 1 to create a new UniformProposer object from inherited base classes: Proposer with example: obj = UniformProposer(use_depth)