class_docstr: Dimensioning properties
docstr: Passes a normalization parameter to the underlying implementation for any
  necessary transformations.
  
  TODO(cmikeh2): In the future we'll distinguish between the different norm objects,
  but for now we'll just use the same one for all of them.
  
  Args:
      param (torch.Tensor): The parameter to transform. This may be either a bias or weight and should have
          shape (model_dim,)
docstr_tok: ["Passes","a","normalization","parameter","to","the","underlying","implementation","for","any","necessary","transformations",".","TODO","(","cmikeh2",")",":","In","the","future","we","'ll","distinguish","between","the","different","norm","objects",",","but","for","now","we","'ll","just","use","the","same","one","for","all","of","them",".","Args",":","param","(","torch.Tensor",")",":","The","parameter","to","transform",".","This","may","be","either","a","bias","or","weight","and","should","have","shape","(","model_dim",",",")"]
code: def transform_norm_param(
      self, param: torch.Tensor
  ) -> InferenceParameter:
      """
      Passes a normalization parameter to the underlying implementation for any
      necessary transformations.
  
      TODO(cmikeh2): In the future we'll distinguish between the different norm objects,
      but for now we'll just use the same one for all of them.
  
      Args:
          param (torch.Tensor): The parameter to transform. This may be either a bias or weight and should have
              shape (model_dim,)
      """
      return self.norm.transform_param(param)
  
imports: from abc import abstractmethod
  from typing import Optional
  import torch
  from deepspeed.accelerator import get_accelerator
  from ..config_v2 import RaggedInferenceEngineConfig
  from ..inference_utils import ActivationType, ceil_div, is_gated
  from ..model_implementations import *
  from ..model_implementations.sharding import *
  from ..modules.configs import DSEmbeddingsConfig, DSLinearConfig, DSMoEConfig, DSNormConfig, DSSelfAttentionConfig, DSUnembedConfig, NormTypeEnum, PositionalEmbeddingType, RotateHalfConfig
  from ..modules import heuristics
  from ..ragged import DSSequenceDescriptor, KVCacheConfig, RaggedBatchWrapper
  from .inference_model_base import DSInferenceModelBase, DSModelImplementationConfig, MPType
  from ..inference_parameter import InferenceParameter
label_desc: Use image node_id 36 for calling the DSTransformerModelBase obj's underlying member method code with example usage: obj.transform_norm_param(param) and returns: self