code: def dla46x_c(pretrained=False, **kwargs) -> DLA:  # DLA-X-46-C
      model_args = dict(
          levels=[1, 1, 1, 2, 2, 1],
          channels=[16, 32, 64, 64, 128, 256],
          block=DlaBottleneck,
          cardinality=32,
          base_width=4,
      )
      return _create_dla(
          "dla46x_c", 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 7 for calling a global function with example usage: dla46x_c(pretrained) and returns: _create_dla