code: def get_model(name, **kwargs):
      # resnet
      if name == "r18":
          return iresnet18(False, **kwargs)
      elif name == "r34":
          return iresnet34(False, **kwargs)
      elif name == "r50":
          return iresnet50(False, **kwargs)
      elif name == "r100":
          return iresnet100(False, **kwargs)
      elif name == "r200":
          return iresnet200(False, **kwargs)
      elif name == "r2060":
          from .iresnet2060 import iresnet2060
  
          return iresnet2060(False, **kwargs)
  
      elif name == "mbf":
          fp16 = kwargs.get("fp16", False)
          num_features = kwargs.get("num_features", 512)
          return get_mbf(fp16=fp16, num_features=num_features)
  
      elif name == "mbf_large":
          from .mobilefacenet import get_mbf_large
  
          fp16 = kwargs.get("fp16", False)
          num_features = kwargs.get("num_features", 512)
          return get_mbf_large(fp16=fp16, num_features=num_features)
  
      elif name == "vit_t":
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=256,
              depth=12,
              num_heads=8,
              drop_path_rate=0.1,
              norm_layer="ln",
              mask_ratio=0.1,
          )
  
      elif name == "vit_t_dp005_mask0":  # For WebFace42M
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=256,
              depth=12,
              num_heads=8,
              drop_path_rate=0.05,
              norm_layer="ln",
              mask_ratio=0.0,
          )
  
      elif name == "vit_s":
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=512,
              depth=12,
              num_heads=8,
              drop_path_rate=0.1,
              norm_layer="ln",
              mask_ratio=0.1,
          )
  
      elif name == "vit_s_dp005_mask_0":  # For WebFace42M
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=512,
              depth=12,
              num_heads=8,
              drop_path_rate=0.05,
              norm_layer="ln",
              mask_ratio=0.0,
          )
  
      elif name == "vit_b":
          # this is a feature
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=512,
              depth=24,
              num_heads=8,
              drop_path_rate=0.1,
              norm_layer="ln",
              mask_ratio=0.1,
              using_checkpoint=True,
          )
  
      elif name == "vit_b_dp005_mask_005":  # For WebFace42M
          # this is a feature
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=512,
              depth=24,
              num_heads=8,
              drop_path_rate=0.05,
              norm_layer="ln",
              mask_ratio=0.05,
              using_checkpoint=True,
          )
  
      elif name == "vit_l_dp005_mask_005":  # For WebFace42M
          # this is a feature
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=768,
              depth=24,
              num_heads=8,
              drop_path_rate=0.05,
              norm_layer="ln",
              mask_ratio=0.05,
              using_checkpoint=True,
          )
  
      elif name == "vit_h":  # For WebFace42M
          num_features = kwargs.get("num_features", 512)
          from .vit import VisionTransformer
  
          return VisionTransformer(
              img_size=112,
              patch_size=9,
              num_classes=num_features,
              embed_dim=1024,
              depth=48,
              num_heads=8,
              drop_path_rate=0.1,
              norm_layer="ln",
              mask_ratio=0,
              using_checkpoint=True,
          )
  
      else:
          raise ValueError()
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(name) and returns: iresnet18, iresnet34, iresnet50, iresnet100, iresnet200, iresnet2060, get_mbf, get_mbf_large, VisionTransformer, VisionTransformer, VisionTransformer, VisionTransformer, VisionTransformer, VisionTransformer, VisionTransformer, VisionTransformer