code: def _create_efficientvit_msra(variant, pretrained=False, **kwargs):
      out_indices = kwargs.pop("out_indices", (0, 1, 2))
      model = build_model_with_cfg(
          EfficientVitMsra,
          variant,
          pretrained,
          feature_cfg=dict(
              flatten_sequential=True, out_indices=out_indices
          ),
          **kwargs,
      )
      return model
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  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_efficientvit_msra(variant, pretrained) and returns: model