code: def create_surrogate(
      name: str,
      tracker: ModelTracker,
      predict: Optional[List[str]],
      input_flags: Dict[str, bool],
      **kwargs: Any,
  ) -> Surrogate[ModelConfig]:
      """
      Creates a surrogate using the specified parameters.
      """
      assert name in SURROGATE_REGISTRY, f"Unknown surrogate {name}."
  
      # Build the parameters
      surrogate_cls = SURROGATE_REGISTRY[name]
      args = {"predict": predict, "tracker": tracker, **kwargs}
      if issubclass(surrogate_cls, DatasetFeaturesMixin):
          args.update(input_flags)
  
      # Initialize the surrogate
      return surrogate_cls(**args)
  
imports: from typing import Any, Callable, Dict, List, Optional, Type, TypeVar
  from tsbench.config import EnsembleConfig, ModelConfig
  from tsbench.evaluations.tracking import EnsembleTracker, ModelTracker
  from ._base import DatasetFeaturesMixin, Surrogate
label_desc: Use image node_id 3 for calling a global function with example usage: create_surrogate(name, tracker, predict, input_flags) and returns: surrogate_cls