code: def __init__(self, in_channels, num_anchors, num_classes):
      self.num_classes = num_classes
      self.conv = flatten(
          [
              (
                  nn.Conv2d(
                      in_channels, in_channels, kernel_size=3, padding=1
                  ),
                  lambda x: x.relu(),
              )
              for _ in range(4)
          ]
      )
      self.cls_logits = nn.Conv2d(
          in_channels,
          num_anchors * num_classes,
          kernel_size=3,
          padding=1,
      )
  
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 ClassificationHead object  with example: obj = ClassificationHead(in_channels, num_anchors, num_classes)