code: def _create_pit(variant, pretrained=False, **kwargs):
      default_out_indices = tuple(range(3))
      out_indices = kwargs.pop("out_indices", default_out_indices)
  
      model = build_model_with_cfg(
          PoolingVisionTransformer,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          feature_cfg=dict(
              feature_cls="hook",
              no_rewrite=True,
              out_indices=out_indices,
          ),
          **kwargs,
      )
      return model
  
imports: import math
  import re
  from functools import partial
  from typing import Sequence, Tuple
  import torch
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_, to_2tuple, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 2 for calling a global function with example usage: _create_pit(variant, pretrained) and returns: model