code: def ldm_semantic(pretrained: bool = True) -> TLoadZoo:
      return ldm_vq(
          pretrained=pretrained,
          latent_size=128,
          latent_in_channels=6,
          download_name="ldm_semantic",
          model_config=dict(
              ema_decay=None,
              start_channels=128,
              num_heads=8,
              num_head_channels=None,
              attention_downsample_rates=[8, 16, 32],
              channel_multipliers=[1, 4, 8],
              condition_type=CONCAT_TYPE,
              condition_model="rescaler",
              condition_config=dict(
                  num_stages=2,
                  in_channels=182,
                  out_channels=3,
              ),
              first_stage_config=dict(
                  pretrained=False,
              ),
          ),
      )
  
imports: from typing import Any
  from typing import Union
  from cftool.types import tensor_dict_type
  from ...schema import DLConfig
  from ...zoo.core import DLZoo
  from ...zoo.core import TPipeline
  from ...models.cv.diffusion.utils import CONCAT_TYPE
label_desc: Use image node_id 17 for calling a global function with example usage: ldm_semantic(pretrained) and returns: ldm_vq