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 reset_mlp(self):
      self._h4h_w.data[
          : self.inter_up_w.shape[0]
      ] = self.inter_up_w.data
      self._h4h_w.data[
          self.inter_up_w.shape[0] :
      ] = self.inter_gate_w.data
  
      if self.inter_up_b is not None:
          self._h4h_b.data[
              : self.inter_up_b.shape[0]
          ] = self.inter_up_b.data
          self._h4h_b.data[
              self.inter_up_b.shape[0] :
          ] = self.inter_gate_b.data
  
      inter_data = [self.inter_up_w.data, self.inter_gate_w.data]
      if self.inter_up_b is not None:
          inter_data.extend(
              [self.inter_up_b.data, self.inter_gate_b.data]
          )
  
      self.inter_up_w.data = self._h4h_w.data[
          : self.inter_up_w.shape[0]
      ]
      self.inter_gate_w.data = self._h4h_w.data[
          self.inter_up_w.shape[0] :
      ]
  
      if self.inter_up_b is not None:
          self.inter_up_b.data = self._h4h_b.data[
              : self.inter_up_b.shape[0]
          ]
          self.inter_gate_b.data = self._h4h_b.data[
              self.inter_up_b.shape[0] :
          ]
  
      for data in inter_data:
          del data
  
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 5 for calling the HybridGatedMLPContainer obj's underlying member method code with example usage: obj.reset_mlp() without return types