docstr: function to be minimised:
  f(x) = (x[0]-5)^2 + x[1]^2 + (x[2]-1)^2
  Solution:
  x = [5,0,1]
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code: def f(self):
      """
      function to be minimised:
      f(x) = (x[0]-5)^2 + x[1]^2 + (x[2]-1)^2
      Solution:
      x = [5,0,1]
      """
      out = torch.zeros_like(self.pars)
      out[0] = self.pars[0] - 5
      out[1] = self.pars[1]
      out[2] = self.pars[2] - 1
      return torch.sum(out**2)
  
imports: import unittest
  import torch
  from torch.nn import Module, Linear
  import torch.nn.init
  import torchbearer
  from torchbearer import callbacks
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.f() and returns: torch