code: def efficientvit_m5(pretrained=False, **kwargs):
      model_args = dict(
          img_size=224,
          embed_dim=[192, 288, 384],
          depth=[1, 3, 4],
          num_heads=[3, 3, 4],
          window_size=[7, 7, 7],
          kernels=[7, 5, 3, 3],
      )
      return _create_efficientvit_msra(
          "efficientvit_m5",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
  
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 8 for calling a global function with example usage: efficientvit_m5(pretrained) and returns: _create_efficientvit_msra