code: def _create_inception_v3(variant, pretrained=False, **kwargs):
      pretrained_cfg = resolve_pretrained_cfg(
          variant, pretrained_cfg=kwargs.pop("pretrained_cfg", None)
      )
      aux_logits = kwargs.get("aux_logits", False)
      has_aux_logits = False
      if pretrained_cfg:
          # only torchvision pretrained weights have aux logits
          has_aux_logits = pretrained_cfg.tag == "tv_in1k"
      if aux_logits:
          assert not kwargs.pop("features_only", False)
          load_strict = has_aux_logits
      else:
          load_strict = not has_aux_logits
  
      return build_model_with_cfg(
          InceptionV3,
          variant,
          pretrained,
          pretrained_cfg=pretrained_cfg,
          pretrained_strict=load_strict,
          **kwargs,
      )
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import trunc_normal_, create_classifier, Linear, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._builder import resolve_pretrained_cfg
  from ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 1 for calling a global function with example usage: _create_inception_v3(variant, pretrained) and returns: build_model_with_cfg