code: def _check_lr(name, optimizer, lr):
      """Return one learning rate for each param group."""
      n = len(optimizer.param_groups)
      if not isinstance(lr, (list, tuple)):
          return lr * np.ones(n)
  
      if len(lr) != n:
          raise ValueError(
              "{} lr values were passed for {} but there are "
              "{} param groups.".format(n, name, len(lr))
          )
      return np.array(lr)
  
imports: import sys
  import warnings
  import numpy
  import torch
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torch.optim.lr_scheduler import CyclicLR
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.optim.lr_scheduler import LambdaLR
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.optim.lr_scheduler import StepLR
  from torch.optim.optimizer import Optimizer
  from skorch.callbacks import Callback
label_desc: Use image node_id 1 for calling a global function with example usage: _check_lr(name, optimizer, lr) and returns: np, unknown