code: def __call__(self, input):
      se = input.avg_pool2d(
          kernel_size=(input.shape[2], input.shape[3])
      )  # GlobalAveragePool2D
      se = se.reshape(shape=(-1, self.filters))
      se = se.dot(self.weight1) + self.bias1
      se = se.relu()
      se = se.dot(self.weight2) + self.bias2
      se = se.sigmoid().reshape(
          shape=(-1, self.filters, 1, 1)
      )  # for broadcasting
      se = input.mul(se)
      return se
  
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 2 for calling the SqueezeExciteBlock2D obj's underlying member method code with example usage: obj.__call__(input) and returns: se