code: def get_optimizer_class(
      clipping: str, distributed: bool, grad_sample_mode: str = None
  ):
      if clipping == "flat" and distributed is False:
          return DPOptimizer
      elif clipping == "flat" and distributed is True:
          return DistributedDPOptimizer
      elif clipping == "per_layer" and distributed is False:
          return DPPerLayerOptimizer
      elif clipping == "per_layer" and distributed is True:
          if grad_sample_mode == "hooks":
              return DistributedPerLayerOptimizer
          elif grad_sample_mode == "ew":
              return SimpleDistributedPerLayerOptimizer
          else:
              raise ValueError(
                  f"Unexpected grad_sample_mode: {grad_sample_mode}"
              )
      elif clipping == "adaptive" and distributed is False:
          return AdaClipDPOptimizer
      raise ValueError(
          f"Unexpected optimizer parameters. Clipping: {clipping}, distributed: {distributed}"
      )
  
imports: from .adaclipoptimizer import AdaClipDPOptimizer
  from .ddp_perlayeroptimizer import DistributedPerLayerOptimizer, SimpleDistributedPerLayerOptimizer
  from .ddpoptimizer import DistributedDPOptimizer
  from .optimizer import DPOptimizer
  from .perlayeroptimizer import DPPerLayerOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: get_optimizer_class(clipping, distributed, grad_sample_mode) and returns: DPOptimizer, DistributedDPOptimizer, DPPerLayerOptimizer, DistributedPerLayerOptimizer, AdaClipDPOptimizer, SimpleDistributedPerLayerOptimizer