code: def set_transformer_params(
      self,
      prefixes: Union[str, Iterable[str]],
      containers: List[LayerContainer],
  ) -> None:
      if not isinstance(containers, list):
          raise ValueError(
              f"The transformer containers should be a list, of one container per layer, but got {type(containers)} instead."
          )
  
      self._transformer_prefixes = (
          prefixes if isinstance(prefixes, list) else [prefixes]
      )
      self._transformer_params = containers
  
imports: import json
  from abc import ABC, ABCMeta, abstractmethod
  from typing import Any, Iterable, List, Optional, Union
  import torch
  from ..config_v2 import RaggedInferenceEngineConfig
  from ..checkpoint import CheckpointEngineBase
  from ..logging import inference_logger
  from .layer_container_base import LayerContainer
  from .inference_model_base import DSInferenceModelBase
  from .flat_model_helpers import flatten_inference_model, make_param_filename, make_metadata_filename, ModelMetadata, restore_inference_model
label_desc: Use image node_id 4 for calling the ContainerMap obj's underlying member method code with example usage: obj.set_transformer_params(prefixes, containers) without return types