code: def create_timm_model(
      arch,
      n_out,
      cut=None,
      pretrained=True,
      n_in=3,
      init=nn.init.kaiming_normal_,
      custom_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,
      **kwargs,
  ):
      "Create custom architecture using `arch`, `n_in` and `n_out` from the `timm` library"
      model = timm.create_model(
          arch,
          pretrained=pretrained,
          num_classes=0,
          in_chans=n_in,
          **kwargs,
      )
      body = TimmBody(model, pretrained, None, n_in)
      nf = body.model.num_features
      res = add_head(
          body,
          nf,
          n_out,
          init=init,
          head=custom_head,
          concat_pool=concat_pool,
          pool=body.needs_pool,
          lin_ftrs=lin_ftrs,
          ps=ps,
          first_bn=first_bn,
          bn_final=bn_final,
          lin_first=lin_first,
          y_range=y_range,
      )
      return res, model.default_cfg
  
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 18 for calling a global function with example usage: create_timm_model(arch, n_out, cut, pretrained, n_in, init, custom_head, concat_pool, pool, lin_ftrs, ps, first_bn, bn_final, lin_first, y_range) and returns: res,  model