code: def add_head(
      body,
      nf,
      n_out,
      init=nn.init.kaiming_normal_,
      head=None,
      concat_pool=True,
      pool=True,
      lin_ftrs=None,
      ps=0.5,
      first_bn=True,
      bn_final=False,
      lin_first=False,
      y_range=None,
  ):
      "Add a head to a vision body"
      if head is None:
          head = create_head(
              nf,
              n_out,
              concat_pool=concat_pool,
              pool=pool,
              lin_ftrs=lin_ftrs,
              ps=ps,
              first_bn=first_bn,
              bn_final=bn_final,
              lin_first=lin_first,
              y_range=y_range,
          )
      model = nn.Sequential(body, head)
      if init is not None:
          apply_init(model[1], init)
      return model
  
imports: from __future__ import annotations
  from packaging.version import parse
  from ..basics import *
  from .core import *
  from .data import *
  from .augment import *
  from .None import models
  import torchvision
  from ..callback.hook import num_features_model
label_desc: Use image node_id 16 for calling a global function with example usage: add_head(body, nf, n_out, init, head, concat_pool, pool, lin_ftrs, ps, first_bn, bn_final, lin_first, y_range) and returns: model