code: def load_weights(self, url):
      weights = np.frombuffer(
          fetch(url).read_bytes(), dtype=np.float32
      )[5:]
      ptr = 0
      for i in range(len(self.module_list)):
          module_type = self.blocks[i + 1]["type"]
          if module_type == "convolutional":
              model = self.module_list[i]
              try:  # we have batchnorm, load conv weights without biases, and batchnorm values
                  batch_normalize = int(
                      self.blocks[i + 1]["batch_normalize"]
                  )
              except:  # no batchnorm, load conv weights + biases
                  batch_normalize = 0
              conv = model[0]
              if batch_normalize:
                  bn = model[1]
                  # Get the number of weights of batchnorm
                  num_bn_biases = math.prod(bn.bias.shape)
                  # Load weights
                  bn_biases = Tensor(weights[ptr : ptr + num_bn_biases])
                  ptr += num_bn_biases
                  bn_weights = Tensor(
                      weights[ptr : ptr + num_bn_biases]
                  )
                  ptr += num_bn_biases
                  bn_running_mean = Tensor(
                      weights[ptr : ptr + num_bn_biases]
                  )
                  ptr += num_bn_biases
                  bn_running_var = Tensor(
                      weights[ptr : ptr + num_bn_biases]
                  )
                  ptr += num_bn_biases
                  # Cast the loaded weights into dims of model weights
                  bn_biases = bn_biases.reshape(
                      shape=tuple(bn.bias.shape)
                  )
                  bn_weights = bn_weights.reshape(
                      shape=tuple(bn.weight.shape)
                  )
                  bn_running_mean = bn_running_mean.reshape(
                      shape=tuple(bn.running_mean.shape)
                  )
                  bn_running_var = bn_running_var.reshape(
                      shape=tuple(bn.running_var.shape)
                  )
                  # Copy data
                  bn.bias = bn_biases
                  bn.weight = bn_weights
                  bn.running_mean = bn_running_mean
                  bn.running_var = bn_running_var
              else:
                  # load biases of the conv layer
                  num_biases = math.prod(conv.bias.shape)
                  # Load weights
                  conv_biases = Tensor(weights[ptr : ptr + num_biases])
                  ptr += num_biases
                  # Reshape
                  conv_biases = conv_biases.reshape(
                      shape=tuple(conv.bias.shape)
                  )
                  # Copy
                  conv.bias = conv_biases
              # Load weighys for conv layers
              num_weights = math.prod(conv.weight.shape)
              conv_weights = Tensor(weights[ptr : ptr + num_weights])
              ptr += num_weights
              conv_weights = conv_weights.reshape(
                  shape=tuple(conv.weight.shape)
              )
              conv.weight = conv_weights
  
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 4 for calling the Darknet obj's underlying member method code with example usage: obj.load_weights(url) without return types