code: def optimizer_resolver(query: Union[Any, str], *args, **kwargs):
      base_cls = Optimizer
      optimizers = [
          optimizer
          for optimizer in vars(torch.optim).values()
          if isinstance(optimizer, type)
          and issubclass(optimizer, base_cls)
      ]
      return resolver(
          optimizers, {}, query, base_cls, None, *args, **kwargs
      )
  
file_path: pytorch_geometric/torch_geometric/nn/resolver.py
imports: import inspect
  from typing import Any, Optional, Union
  import torch
  from torch import Tensor
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch_geometric.nn.lr_scheduler import ConstantWithWarmupLR, CosineWithWarmupLR, CosineWithWarmupRestartsLR, LinearWithWarmupLR, PolynomialWithWarmupLR
  from torch_geometric.resolver import normalize_string, resolver
label_desc: Use image node_id 5 for calling a global function with example usage: optimizer_resolver(query) and returns: resolver