class_docstr: Inference model implementation for ragged batching for OPT models.
class_docstr_tok: ["Inference","model","implementation","for","ragged","batching","for","OPT","models","."]
docstr: Performs setup and creates embedding DSModule. Since OPT includes trained
  positional embeddings, we will override the base model implementation.
docstr_tok: ["Performs","setup","and","creates","embedding","DSModule",".","Since","OPT","includes","trained","positional","embeddings",",","we","will","override","the","base","model","implementation","."]
code: def make_embedding_layer(self) -> None:
      """
      Performs setup and creates embedding DSModule. Since OPT includes trained
      positional embeddings, we will override the base model implementation.
      """
  
      embed_config = DSEmbeddingsConfig(
          max_tokens=self._engine_config.state_manager.max_ragged_batch_size,
          residual_dtype=self.activation_dtype,
          embedding_dim=self.model_dim,
          positional_embedding=True,
          positional_offset=2,
      )
  
      self.embed = instantiate_embed(embed_config, self._engine_config)
  
imports: from typing import Iterable, Optional, Tuple
  import torch
  import deepspeed.comm
  from ...allocator import empty_from
  from ...inference_utils import ActivationType, DtypeEnum
  from ...model_implementations import *
  from ...modules.configs import DSEmbeddingsConfig, NormTypeEnum, PositionalEmbeddingType
  from ...ragged import RaggedBatchWrapper
  from .container import OPTNonTransformerContainer, OPTTransformerContainer
  from ...modules.heuristics import instantiate_embed
label_desc: Use image node_id 12 for calling the OPTInferenceModel obj's underlying member method code with example usage: obj.make_embedding_layer() without return types