class_docstr: Same, but norm is trainable
      
class_docstr_tok: ["Same",",","but","norm","is","trainable"]
code: def __init__(self, norm=3, output_size=1, eps=1e-6):
      super().__init__(norm, output_size, eps)
      self.p = nn.Parameter(torch.ones(1) * norm)
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new GeneralizedMeanPoolingP object from inherited base classes: GeneralizedMeanPooling with example: obj = GeneralizedMeanPoolingP(norm, output_size, eps)