code: def feedback(
      self,
      partitionable: bool,
      plan: Optional[List[ShardingOption]] = None,
      perf_rating: Optional[float] = None,
      storage_constraint: Optional[Topology] = None,
  ) -> None:
      # static strategy, ignore feedback and just provide next proposal
      self._proposal_index += 1
  
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 5 for calling the GridSearchProposer obj's underlying member method code with example usage: obj.feedback(partitionable, plan, perf_rating, storage_constraint) without return types