code: def crossvit_15_240(pretrained=False, **kwargs) -> CrossVit:
      model_args = dict(
          img_scale=(1.0, 224 / 240),
          patch_size=[12, 16],
          embed_dim=[192, 384],
          depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
          num_heads=[6, 6],
          mlp_ratio=[3, 3, 1],
      )
      model = _create_crossvit(
          variant="crossvit_15_240",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
      return model
  
imports: from functools import partial
  from typing import List
  from typing import Tuple
  import torch
  import torch.hub
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 9 for calling a global function with example usage: crossvit_15_240(pretrained) and returns: model