class_docstr: The HybridGatedMLPContainer supports models for which the first MLP layer
  is represented with two separate weights, one for the activation function
  and one for the gating function.
class_docstr_tok: ["The","HybridGatedMLPContainer","supports","models","for","which","the","first","MLP","layer","is","represented","with","two","separate","weights",",","one","for","the","activation","function","and","one","for","the","gating","function","."]
code: def get_mlp_params(self):
      params = super().get_mlp_params()
      params.extend(
          [
              self.inter_up_w,
              self.inter_up_b,
              self.inter_gate_w,
              self.inter_gate_b,
          ]
      )
      return params
  
file_path: DeepSpeed/deepspeed/module_inject/containers/features/gated_mlp.py
imports: from abc import abstractmethod
  from .hybrid_engine import HybridEngineContainer
label_desc: Use image node_id 7 for calling the HybridGatedMLPContainer obj's underlying member method code with example usage: obj.get_mlp_params() and returns: params