code: def dla102x2(pretrained=False, **kwargs) -> DLA:  # DLA-X-102 64
      model_args = dict(
          levels=[1, 1, 1, 3, 4, 1],
          channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck,
          cardinality=64,
          base_width=4,
          shortcut_root=True,
      )
      return _create_dla(
          "dla102x2", pretrained, **dict(model_args, **kwargs)
      )
  
imports: import math
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 13 for calling a global function with example usage: dla102x2(pretrained) and returns: _create_dla