code: def update(self, alphas_normal, alphas_reduce, val_loss):
      import torch.nn.functional as F
  
      a_normal = F.softmax(alphas_normal, dim=-1)
      # print("alpha normal size: ", a_normal.data.size())
      a_reduce = F.softmax(alphas_reduce, dim=-1)
      # print("alpha reduce size: ", a_reduce.data.size())
      data = np.concatenate(
          [
              a_normal.data.view(-1),
              a_reduce.data.view(-1),
              np.array([val_loss.data]),
          ]
      ).reshape(1, -1)
      if self.data is not None:
          self.data = np.concatenate([self.data, data], axis=0)
      else:
          self.data = data
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 2 for calling the Performance obj's underlying member method code with example usage: obj.update(alphas_normal, alphas_reduce, val_loss) without return types