class_docstr: Dimensioning properties
docstr: Return a boolean to determine whether the model uses a gated activation function.
docstr_tok: ["Return","a","boolean","to","determine","whether","the","model","uses","a","gated","activation","function","."]
code: def gated_mlp(self) -> bool:
      """
      Return a boolean to determine whether the model uses a gated activation function.
      """
      return is_gated(self.mlp_activation_fn)
  
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 15 for calling the DSTransformerModelBase obj's underlying member method code with example usage: obj.gated_mlp() and returns: is_gated