code: def create_head(
      nf,
      n_out,
      lin_ftrs=None,
      ps=0.5,
      pool=True,
      concat_pool=True,
      first_bn=True,
      bn_final=False,
      lin_first=False,
      y_range=None,
  ):
      "Model head that takes `nf` features, runs through `lin_ftrs`, and out `n_out` classes."
      if pool and concat_pool:
          nf *= 2
      lin_ftrs = (
          [nf, 512, n_out]
          if lin_ftrs is None
          else [nf] + lin_ftrs + [n_out]
      )
      bns = [first_bn] + [True] * len(lin_ftrs[1:])
      ps = L(ps)
      if len(ps) == 1:
          ps = [ps[0] / 2] * (len(lin_ftrs) - 2) + ps
      actns = [nn.ReLU(inplace=True)] * (len(lin_ftrs) - 2) + [None]
      layers = []
      if pool:
          pool = (
              AdaptiveConcatPool2d()
              if concat_pool
              else nn.AdaptiveAvgPool2d(1)
          )
          layers += [pool, Flatten()]
      if lin_first:
          layers.append(nn.Dropout(ps.pop(0)))
      for ni, no, bn, p, actn in zip(
          lin_ftrs[:-1], lin_ftrs[1:], bns, ps, actns
      ):
          layers += LinBnDrop(
              ni, no, bn=bn, p=p, act=actn, lin_first=lin_first
          )
      if lin_first:
          layers.append(nn.Linear(lin_ftrs[-2], n_out))
      if bn_final:
          layers.append(nn.BatchNorm1d(lin_ftrs[-1], momentum=0.01))
      if y_range is not None:
          layers.append(SigmoidRange(*y_range))
      return nn.Sequential(*layers)
  
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 8 for calling a global function with example usage: create_head(nf, n_out, lin_ftrs, ps, pool, concat_pool, first_bn, bn_final, lin_first, y_range) and returns: nn