code: def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
      """
      Constructs a GhostNet model
      """
      cfgs = [
          # k, t, c, SE, s
          # stage1
          [[3, 16, 16, 0, 1]],
          # stage2
          [[3, 48, 24, 0, 2]],
          [[3, 72, 24, 0, 1]],
          # stage3
          [[5, 72, 40, 0.25, 2]],
          [[5, 120, 40, 0.25, 1]],
          # stage4
          [[3, 240, 80, 0, 2]],
          [
              [3, 200, 80, 0, 1],
              [3, 184, 80, 0, 1],
              [3, 184, 80, 0, 1],
              [3, 480, 112, 0.25, 1],
              [3, 672, 112, 0.25, 1],
          ],
          # stage5
          [[5, 672, 160, 0.25, 2]],
          [
              [5, 960, 160, 0, 1],
              [5, 960, 160, 0.25, 1],
              [5, 960, 160, 0, 1],
              [5, 960, 160, 0.25, 1],
          ],
      ]
      model_kwargs = dict(
          cfgs=cfgs,
          width=width,
          **kwargs,
      )
      return build_model_with_cfg(
          GhostNet,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          feature_cfg=dict(flatten_sequential=True),
          **model_kwargs,
      )
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectAdaptivePool2d, Linear, make_divisible
  from ._builder import build_model_with_cfg
  from ._efficientnet_blocks import SqueezeExcite, ConvBnAct
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling a global function with example usage: _create_ghostnet(variant, width, pretrained) and returns: build_model_with_cfg