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 release_mlp(self):
      super().release_mlp()
      gated_mlp_params = [
          (self.module.mlp.inter_up_w, self.inter_up_w),
          (self.module.mlp.inter_up_b, self.inter_up_b),
          (self.module.mlp.inter_gate_w, self.inter_gate_w),
          (self.module.mlp.inter_gate_b, self.inter_gate_b),
      ]
  
      self._release_params(gated_mlp_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 4 for calling the HybridGatedMLPContainer obj's underlying member method code with example usage: obj.release_mlp() without return types