code: def _create_swin_transformer(variant, pretrained=False, **kwargs):
      default_out_indices = tuple(
          i for i, _ in enumerate(kwargs.get("depths", (1, 1, 3, 1)))
      )
      out_indices = kwargs.pop("out_indices", default_out_indices)
  
      model = build_model_with_cfg(
          SwinTransformer,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          feature_cfg=dict(
              flatten_sequential=True, out_indices=out_indices
          ),
          **kwargs,
      )
  
      return model
  
imports: import logging
  import math
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, _assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
  from .vision_transformer import get_init_weights_vit
label_desc: Use image node_id 5 for calling a global function with example usage: _create_swin_transformer(variant, pretrained) and returns: model