class_docstr: Dimensioning properties
docstr: Performs setup and creates embedding DSModule. This will set the `self.embed` attribute.
docstr_tok: ["Performs","setup","and","creates","embedding","DSModule",".","This","will","set","the","`","self.embed","`","attribute","."]
code: def make_embedding_layer(self) -> None:
      """
      Performs setup and creates embedding DSModule. This will set the `self.embed` attribute.
      """
  
      embed_config = DSEmbeddingsConfig(
          max_tokens=self._engine_config.state_manager.max_ragged_batch_size,
          residual_dtype=self.activation_dtype,
          embedding_dim=self.model_dim,
      )
  
      self.embed = heuristics.instantiate_embed(
          embed_config, self._engine_config
      )
  
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 17 for calling the DSTransformerModelBase obj's underlying member method code with example usage: obj.make_embedding_layer() without return types