code: def create_optimizer_v2a(
      model: nn.Module,
      optimizer_name: str = "sgd",
      learning_rate: Optional[float] = None,
      weight_decay: float = 0.0,
      momentum: float = 0.9,
      filter_bias_and_bn: bool = True,
      feature_lr_mult: float = 0.01,
      fc_lr_mult: float = 1,
      **kwargs,
  ):
      """Create an optimizer.
      Note that this version is modifed based on
      https://github.com/rwightman/pytorch-image-models/blob/cd3dc4979f6ca16a09910b4a32b7a8f07cc31fda/timm/optim/optim_factory.py#L73
      It allows feature backbone and output linear layers to have different learning rate by having more groups
      TODO currently the model is passed in and all parameters are selected for optimization.
      For more general use an interface that allows selection of parameters to optimize and lr groups, one of:
        * a filter fn interface that further breaks params into groups in a weight_decay compatible fashion
        * expose the parameters interface and leave it up to caller
      Args:
          model (nn.Module): model containing parameters to optimize
          optimizer_name: name of optimizer to create
          learning_rate: initial learning rate
          weight_decay: weight decay to apply in optimizer
          momentum:  momentum for momentum based optimizers (others may use betas via kwargs)
          filter_bias_and_bn:  filter out bias, bn and other 1d params from weight decay
          **kwargs: extra optimizer specific kwargs to pass through
      Returns:
          Optimizer
      """
      opt_lower = optimizer_name.lower()
      if weight_decay and filter_bias_and_bn:
          skip = {}
          if hasattr(model, "no_weight_decay"):
              skip = model.no_weight_decay()
          parameters = add_weight_decay_filter_fc(
              model,
              learning_rate,
              weight_decay,
              skip,
              _TIMM_FC_NAMES,
              feature_lr_mult,
              fc_lr_mult,
          )
          weight_decay = 0.0
      else:
          parameters = filter_fc_layer(
              model,
              learning_rate,
              _TIMM_FC_NAMES,
              feature_lr_mult,
              fc_lr_mult,
          )
      if "fused" in opt_lower:
          assert (
              has_apex and torch.cuda.is_available()
          ), "APEX and CUDA required for fused optimizers"
  
      opt_args = dict(
          lr=learning_rate, weight_decay=weight_decay, **kwargs
      )
      opt_split = opt_lower.split("_")
      opt_lower = opt_split[-1]
      if opt_lower in ("sgd", "nesterov"):
          opt_args.pop("eps", None)
          optimizer = optim.SGD(
              parameters, momentum=momentum, nesterov=True, **opt_args
          )
      elif opt_lower == "momentum":
          opt_args.pop("eps", None)
          optimizer = optim.SGD(
              parameters, momentum=momentum, nesterov=False, **opt_args
          )
      elif opt_lower == "adam":
          optimizer = optim.Adam(parameters, **opt_args)
      elif opt_lower == "adabelief":
          optimizer = AdaBelief(parameters, rectify=False, **opt_args)
      elif opt_lower == "adamw":
          optimizer = optim.AdamW(parameters, **opt_args)
      elif opt_lower == "nadam":
          optimizer = Nadam(parameters, **opt_args)
      elif opt_lower == "radam":
          optimizer = RAdam(parameters, **opt_args)
      elif opt_lower == "adamp":
          optimizer = AdamP(
              parameters, wd_ratio=0.01, nesterov=True, **opt_args
          )
      elif opt_lower == "sgdp":
          optimizer = SGDP(
              parameters, momentum=momentum, nesterov=True, **opt_args
          )
      elif opt_lower == "adadelta":
          optimizer = optim.Adadelta(parameters, **opt_args)
      elif opt_lower == "adafactor":
          if not learning_rate:
              opt_args["lr"] = None
          optimizer = Adafactor(parameters, **opt_args)
      elif opt_lower == "adahessian":
          optimizer = Adahessian(parameters, **opt_args)
      elif opt_lower == "rmsprop":
          optimizer = optim.RMSprop(
              parameters, alpha=0.9, momentum=momentum, **opt_args
          )
      elif opt_lower == "rmsproptf":
          optimizer = RMSpropTF(
              parameters, alpha=0.9, momentum=momentum, **opt_args
          )
      elif opt_lower == "novograd":
          optimizer = NovoGrad(parameters, **opt_args)
      elif opt_lower == "nvnovograd":
          optimizer = NvNovoGrad(parameters, **opt_args)
      elif opt_lower == "fusedsgd":
          opt_args.pop("eps", None)
          optimizer = FusedSGD(
              parameters, momentum=momentum, nesterov=True, **opt_args
          )
      elif opt_lower == "fusedmomentum":
          opt_args.pop("eps", None)
          optimizer = FusedSGD(
              parameters, momentum=momentum, nesterov=False, **opt_args
          )
      elif opt_lower == "fusedadam":
          optimizer = FusedAdam(
              parameters, adam_w_mode=False, **opt_args
          )
      elif opt_lower == "fusedadamw":
          optimizer = FusedAdam(
              parameters, adam_w_mode=True, **opt_args
          )
      elif opt_lower == "fusedlamb":
          optimizer = FusedLAMB(parameters, **opt_args)
      elif opt_lower == "fusednovograd":
          opt_args.setdefault("betas", (0.95, 0.98))
          optimizer = FusedNovoGrad(parameters, **opt_args)
      else:
          assert False and "Invalid optimizer"
          raise ValueError
  
      if len(opt_split) > 1:
          if opt_split[0] == "lookahead":
              optimizer = Lookahead(optimizer)
  
      return optimizer
  
imports: from timm.optim.optim_factory import *
label_desc: Use image node_id 4 for calling a global function with example usage: create_optimizer_v2a(model, optimizer_name, learning_rate, weight_decay, momentum, filter_bias_and_bn, feature_lr_mult, fc_lr_mult) and returns: optimizer