docstr: Use the oversized linear as the repeated linear, then take the max
  
  One large linear map makes the implementation simpler and easier for pytorch to make parallel
docstr_tok: ["Use","the","oversized","linear","as","the","repeated","linear",",","then","take","the","max","One","large","linear","map","makes","the","implementation","simpler","and","easier","for","pytorch","to","make","parallel"]
code: def forward(self, inputs):
      """
      Use the oversized linear as the repeated linear, then take the max
  
      One large linear map makes the implementation simpler and easier for pytorch to make parallel
      """
      outputs = self.linear(inputs)
      outputs = outputs.view(
          *outputs.shape[:-1], self.maxout_k, self.out_channels
      )
      outputs = torch.max(outputs, dim=-2)[0]
      return outputs
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the MaxoutLinear obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs