code: def __init__(
      self,
      backbone: ResNet,
      num_classes=264,
      num_anchors=9,
      scales=None,
      aspect_ratios=None,
  ):
      assert isinstance(backbone, ResNet)
      scales = (
          tuple(
              (i, int(i * 2 ** (1 / 3)), int(i * 2 ** (2 / 3)))
              for i in 2 ** np.arange(5, 10)
          )
          if scales is None
          else scales
      )
      aspect_ratios = (
          ((0.5, 1.0, 2.0),) * len(scales)
          if aspect_ratios is None
          else aspect_ratios
      )
      self.num_anchors, self.num_classes = num_anchors, num_classes
      assert len(scales) == len(aspect_ratios) and all(
          self.num_anchors == len(s) * len(ar)
          for s, ar in zip(scales, aspect_ratios)
      )
  
      self.backbone = ResNetFPN(backbone)
      self.head = RetinaHead(
          self.backbone.out_channels,
          num_anchors=num_anchors,
          num_classes=num_classes,
      )
      self.anchor_gen = lambda input_size: generate_anchors(
          input_size,
          self.backbone.compute_grid_sizes(input_size),
          scales,
          aspect_ratios,
      )
  
imports: import math
  from tinygrad.helpers import flatten, get_child
  import tinygrad.nn
  from extra.models.resnet import ResNet
  import numpy
label_desc: Use image node_id 1 to create a new RetinaNet object  with example: obj = RetinaNet(backbone, num_classes, num_anchors, scales, aspect_ratios)