code: def forward(self, x):
      modules = self.blocks[1:]
      outputs = {}  # Cached outputs for route layer
      detections, write = None, False
      for i, module in enumerate(modules):
          module_type = module["type"]
          if (
              module_type == "convolutional"
              or module_type == "upsample"
          ):
              for layer in self.module_list[i]:
                  x = layer(x)
          elif module_type == "route":
              layers = module["layers"]
              layers = [int(a) for a in layers]
              if (layers[0]) > 0:
                  layers[0] = layers[0] - i
              if len(layers) == 1:
                  x = outputs[i + (layers[0])]
              else:
                  if (layers[1]) > 0:
                      layers[1] = layers[1] - i
                  map1 = outputs[i + layers[0]]
                  map2 = outputs[i + layers[1]]
                  x = Tensor(
                      np.concatenate(
                          (map1.numpy(), map2.numpy()), axis=1
                      )
                  )
          elif module_type == "shortcut":
              from_ = int(module["from"])
              x = outputs[i - 1] + outputs[i + from_]
          elif module_type == "yolo":
              anchors = self.module_list[i][0]
              inp_dim = int(self.net_info["height"])  # 416
              num_classes = int(module["classes"])
              x = predict_transform(x, inp_dim, anchors, num_classes)
              if not write:
                  detections, write = x, True
              else:
                  detections = Tensor(
                      np.concatenate(
                          (detections.numpy(), x.numpy()), axis=1
                      )
                  )
          outputs[i] = x
      return detections
  
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 5 for calling the Darknet obj's underlying member method code with example usage: obj.forward(x) and returns: detections