code: def forward(self, x: Tensor):
      x = x.reshape(shape=(-1, 1, 28, 28))  # hacks
      x = self.bn1(x.conv2d(self.c1)).relu().max_pool2d()
      x = self.bn2(x.conv2d(self.c2)).relu().max_pool2d()
      x = x.reshape(shape=[x.shape[0], -1])
      return x.dot(self.l1)
  
imports: import unittest
  import numpy
  from tinygrad.nn.state import get_parameters
  from tinygrad.tensor import Tensor
  from tinygrad.nn import optim, BatchNorm2d
  from extra.training import train, evaluate
  from extra.datasets import fetch_mnist
label_desc: Use image node_id 3 for calling the TinyConvNet obj's underlying member method code with example usage: obj.forward(x) and returns: x