code: def adam_optimizer(
      params: Iterator[Parameter], base_lr: float, weight_decay: float
  ) -> Adam:
      return Adam(params, lr=base_lr, weight_decay=weight_decay)
  
file_path: pytorch_geometric/torch_geometric/graphgym/optim.py
imports: from dataclasses import dataclass, field
  from typing import Any, Iterator, List, Optional
  from torch.nn import Parameter
  from torch.optim import SGD, Adam, Optimizer
  from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR, StepLR
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.config import from_config
label_desc: Use image node_id 1 for calling a global function with example usage: adam_optimizer(params, base_lr, weight_decay) and returns: Adam