code: def forward(self, x):
      x = self.conv[0](x)
      x = self.conv[1](x)
      x = x.avg_pool2d(kernel_size=(2, 2))
      x = self.conv[2](x)
      x1 = x.avg_pool2d(kernel_size=(14, 14)).reshape(
          shape=(-1, 128)
      )  # global
      x2 = x.max_pool2d(kernel_size=(14, 14)).reshape(
          shape=(-1, 128)
      )  # global
      xo = x1.dot(self.weight1) + x2.dot(self.weight2)
      return xo
  
imports: import sys
  import numpy
  from tinygrad.nn.state import get_parameters
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d, optim
  from tinygrad.helpers import getenv
  from extra.datasets import fetch_mnist
  from extra.augment import augment_img
  from extra.training import train, evaluate
label_desc: Use image node_id 5 for calling the BigConvNet obj's underlying member method code with example usage: obj.forward(x) and returns: xo