class_docstr: Dimensioning properties
docstr: Performs embedding sharding along the channels dimension.
docstr_tok: ["Performs","embedding","sharding","along","the","channels","dimension","."]
code: def transform_embedding_param(
      self, param: torch.Tensor
  ) -> InferenceParameter:
      """
      Performs embedding sharding along the channels dimension.
      """
      # Until we can do non-contiguous all-gather, we won't shard the embedding parameters.
      param = param.to(self.activation_dtype.value)
      return InferenceParameter.initialize(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
  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 18 for calling the DSTransformerModelBase obj's underlying member method code with example usage: obj.transform_embedding_param(param) and returns: InferenceParameter