code: def forward(self):
      out = self.x.matmul(self.W).relu()
      # print(out.detach().numpy())
      out = out.log_softmax(1)
      out = out.mul(self.m).add(self.m).sum()
      return out
  
imports: import numpy
  import torch
  import unittest
  from tinygrad import Tensor, Device
  from tinygrad.nn.optim import Adam, SGD, AdamW
  from tinygrad.helpers import CI
label_desc: Use image node_id 2 for calling the TinyNet obj's underlying member method code with example usage: obj.forward() and returns: out