code: def dump_weights(self):
      for i in range(len(self.module_list)):
          module_type = self.blocks[i + 1]["type"]
          if module_type == "convolutional":
              print(self.blocks[i + 1]["type"], "weights", i)
              model = self.module_list[i]
              conv = model[0]
              print(conv.weight.numpy()[0][0][0])
              if conv.bias is not None:
                  print("biases")
                  print(conv.bias.shape)
                  print(conv.bias.numpy()[0][0:5])
              else:
                  print("None biases for layer", i)
  
imports: import sys
  import io
  import time
  import math
  import cv2
  import numpy
  from PIL import Image
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d, Conv2d
  from tinygrad.helpers import fetch
label_desc: Use image node_id 3 for calling the Darknet obj's underlying member method code with example usage: obj.dump_weights() without return types